Date: (Sat) Jun 11, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Classification: Plot AUC Curves for all models & highlight glbMdlSel
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
if (is.null(knitr::opts_current$get(name = 'label'))) # Running in IDE
    debugSource("~/Dropbox/datascience/R/mydsutils.R") else
    source("~/Dropbox/datascience/R/mydsutils.R")    
## Loading required package: caret
## Loading required package: lattice
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 10 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- #NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
'(is.na(glbObsAll[, "Q109244"]) | (glbObsAll[, "Q109244"] != "No"))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
#     
# chk ref value against frequencies vs. alpha sort order
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "D")) 
    
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" 
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024" # Done
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsInteractionOnly[["YOB.Age.dff"]] <- "YOB.Age.fctr"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

# YOB.Age.fctr needs to be synced with YOB.Age.dff; Create a separate sub-function ???
glbFeatsDerive[["YOB.Age.dff"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)

        # retVal <- rep_len(0, length(raw))
        stopifnot(sum(!is.na(raw) && (raw <= 15)) == 0)
        stopifnot(sum(!is.na(raw) && (raw >= 90)) == 0) 
        # msk <- !is.na(raw) && (raw > 15) && (raw <= 20); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 15
        # msk <- !is.na(raw) && (raw > 20) && (raw <= 25); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 20
        # msk <- !is.na(raw) && (raw > 25) && (raw <= 30); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 25
        # msk <- !is.na(raw) && (raw > 30) && (raw <= 35); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 30
        # msk <- !is.na(raw) && (raw > 35) && (raw <= 40); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 35
        # msk <- !is.na(raw) && (raw > 40) && (raw <= 50); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 40
        # msk <- !is.na(raw) && (raw > 50) && (raw <= 65); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 50
        # msk <- !is.na(raw) && (raw > 65) && (raw <= 90); if (sum(msk > 0)) retVal[msk] <- raw[msk] - 65

        breaks <- c(15, 20, 25, 30, 35, 40, 50, 65, 90)        
        retVal <- sapply(raw, function(age) {
            if (is.na(age)) return(0) else
            if ((age > 15) && (age <= 20)) return(age - 15) else
            if ((age > 20) && (age <= 25)) return(age - 20) else
            if ((age > 25) && (age <= 30)) return(age - 25) else
            if ((age > 30) && (age <= 35)) return(age - 30) else
            if ((age > 35) && (age <= 40)) return(age - 35) else
            if ((age > 40) && (age <= 50)) return(age - 40) else
            if ((age > 50) && (age <= 65)) return(age - 50) else
            if ((age > 65) && (age <= 90)) return(age - 65)
        })
        
        return(retVal)
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
                raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
                raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
                raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
                raw1 <- gsub("Idealist"  , "Id", raw1, fixed = TRUE)
                raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glbFeatsCluster <- paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".") # NULL : glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr", 
                     # # "Hhold.fctr",
                     # "Edn.fctr",
                     # paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[", 
#                         toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
#                                       "]\\.[PT]\\."), 
#                                names(glbObsAll), value = TRUE)

glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
# glbRFESizes[["RFE.X"]] <- c(96, 112, 120, 124, 128, 129, 130, 131, 132, 133, 135, 138, 142, 157, 187, 247) # accuracy(131) = 0.6285
# glbRFESizes[["Final"]] <- c(8, 16, 32, 40, 44, 46, 48, 49, 50, 51, 52, 56, 64, 96, 128, 247) # accuracy(49) = 0.6164

glbRFEResults <- NULL

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "Csm.X", "All.X", "Best.Interact") %*% c(NUll, ".NOr", ".Inc")
#   RFE = "Recursive Feature Elimination"
#   Csm = CuStoM
#   NOr = No OutlieRs
#   Inc = INteraCt
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial) {
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") 
} else {
    # glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
    glbMdlFamilies[["All.X"]] <- c("glmnet")    
    # glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm")
    # glbMdlFamilies[["RFE.X"]] <- setdiff(glbMdlMethods, c(NULL
    #     , "bayesglm" # error: Error in trControl$classProbs && any(classLevels != make.names(classLevels)) : invalid 'x' type in 'x && y'
    #     , "lda","lda2" # error: Error in lda.default(x, grouping, ...) : variable 236 appears to be constant within groups
    #     , "svmLinear" # Error in .local(object, ...) : test vector does not match model ! In addition: Warning messages:
    #     , "svmLinear2" # SVM has not been trained using `probability = TRUE`, probabilities not available for predictions
    #     , "svmPoly" # runs 75 models per cv sample for tunelength=5 # took > 2 hrs # Error in .local(object, ...) : test vector does not match model !     
    #     , "svmRadial" # didn't bother
    #     ,"xgbLinear","xgbTree" # Need clang-omp compiler; Upgrade to Revolution R 3.2.3 (3.2.2 current); https://github.com/dmlc/xgboost/issues/276 thread
    #                                     ))
}
# glbMdlFamilies[["All.X.Inc"]] <- glbMdlFamilies[["All.X"]] # value not used
# glbMdlFamilies[["RFE.X.Inc"]] <- glbMdlFamilies[["RFE.X"]] # value not used

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(glbRFEResults)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

# glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
AllX__rcv_glmnetTuneParams <- rbind(data.frame()
    ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
    ,data.frame(parameter = "lambda", vals = "0.0356818417 0.05 0.06367626 0.07 0.09167068")
                        )
FinalAllX__rcv_glmnetTuneParams <- rbind(data.frame()
    ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
    ,data.frame(parameter = "lambda", vals = "6.451187e-03 0.02 2.994376e-02 0.04 0.05343633")
                        )
glbMdlTuneParams <- rbind(glbMdlTuneParams,
    cbind(data.frame(mdlId = "All.X##rcv#glmnet"),            AllX__rcv_glmnetTuneParams),
    cbind(data.frame(mdlId = "Final.All.X##rcv#glmnet"), FinalAllX__rcv_glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
bagEarthTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "degree", vals = "1")
                        ,data.frame(parameter = "nprune", vals = "256")
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "Final.RFE.X.Inc##rcv#bagEarth"),
#                                      bagEarthTuneParams))

# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)

pkgPreprocMethods <-     
# caret version: 6.0.068 # packageVersion("caret")
# operations are applied in this order: zero-variance filter, near-zero variance filter, Box-Cox/Yeo-Johnson/exponential transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign
# *Impute methods needed only if NAs are fed to myfit_mdl
#   Also, ordered.factor in caret creates features as Edn.fctr^4 which is treated as an exponent by bagImpute
    c(NULL
      ,"zv", "nzv"
      ,"BoxCox", "YeoJohnson", "expoTrans"
      ,"center", "scale", "center.scale", "range"
      ,"knnImpute", "bagImpute", "medianImpute"
      ,"zv.pca", "ica", "spatialSign"
      ,"conditionalX") 

glbMdlPreprocMethods <- list(NULL # : default
    # "All.X" = list("glmnet" = union(setdiff(pkgPreprocMethods, 
    #                                           c("knnImpute", "bagImpute", "medianImpute")),
    #                                 # NULL))
    #                                 c("nzv.spatialSign")))    
)
# glbMdlPreprocMethods[["RFE.X"]] <- list("glmnet" = union(unlist(glbMdlPreprocMethods[["All.X"]]),
#                                                     "nzv.pca.spatialSign"))

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "min.elapsedtime.everything",
                           "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB",
              "min.elapsedtime.everything", 
              # "min.aic.fit", 
              "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.elapsedtime.everything")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glbMdlEnsemble <- NULL #"auto"
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- # NULL #: default
    c("Votes_Ensemble_cnk06_out_fin.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "Q109244No_category_cnk01_rest_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- "scrub.data" # NULL # default: script will load envir from previous chunk
glbChunks[["last" ]] <- NULL # NULL # default: script will save envir at end of this chunk 
glbChunks[["inpFilePathName"]] <- "data/Q109244No_category_cnk01_inspect.data_inspect.data.RData" # NULL: default or "data/<prvScriptName>_<lstChunkLbl>.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Temporary: Delete this function (if any) from here after appropriate .RData file is saved

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, 
                             ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))
##        label step_major step_minor label_minor    bgn end elapsed
## 1 scrub.data          1          0           0 10.382  NA      NA

Step 1.0: scrub data

chunk option: eval=

Step 1.0: scrub data

Step 1.0: scrub data

```{r scrub.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        128        622 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         136 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              46             445             177             410 
##           Party         Q124742         Q124122         Q123464 
##              NA            1438             823             708 
##         Q123621         Q122769         Q122770         Q122771 
##             778             644             594             587 
##         Q122120         Q121699         Q121700         Q120978 
##             585             547             563             599 
##         Q121011         Q120379         Q120650         Q120472 
##             571             607             655             649 
##         Q120194         Q120012         Q120014         Q119334 
##             654             591             641             568 
##         Q119851         Q119650         Q118892         Q118117 
##             540             578             486             479 
##         Q118232         Q118233         Q118237         Q117186 
##             701             554             539             648 
##         Q117193         Q116797         Q116881         Q116953 
##             655             590             635             616 
##         Q116601         Q116441         Q116448         Q116197 
##             534             541             560             551 
##         Q115602         Q115777         Q115610         Q115611 
##             539             578             537             487 
##         Q115899         Q115390         Q114961         Q114748 
##             573             619             538             447 
##         Q115195         Q114517         Q114386         Q113992 
##             525             481             521             447 
##         Q114152         Q113583         Q113584         Q113181 
##             537             514             512             453 
##         Q112478         Q112512         Q112270         Q111848 
##             494             460             521             398 
##         Q111580         Q111220         Q110740         Q109367 
##             474             379             357             168 
##         Q108950         Q109244         Q108855         Q108617 
##             204               0             438             288 
##         Q108856         Q108754         Q108342         Q108343 
##             436             338             341             333 
##         Q107869         Q107491         Q106993         Q106997 
##             389             366             389             396 
##         Q106272         Q106388         Q106389         Q106042 
##             426             476             495             451 
##         Q105840         Q105655         Q104996         Q103293 
##             487             393             400             431 
##         Q102906         Q102674         Q102687         Q102289 
##             493             511             475             484 
##         Q102089         Q101162         Q101163         Q101596 
##             462             498             572             477 
##         Q100689         Q100680         Q100562          Q99982 
##             414             497             487             514 
##         Q100010          Q99716          Q99581          Q99480 
##             445             500             466             478 
##          Q98869          Q98578          Q98059          Q98078 
##             564             542             450             569 
##          Q98197          Q96024 
##             528             550
## Warning in rm(pltObsSmp): object 'pltObsSmp' not found
##            label step_major step_minor label_minor    bgn    end elapsed
## 1     scrub.data          1          0           0 10.382 11.298   0.916
## 2 transform.data          1          1           1 11.299     NA      NA

Step 1.1: transform data

##              label step_major step_minor label_minor    bgn    end elapsed
## 2   transform.data          1          1           1 11.299 11.352   0.053
## 3 extract.features          2          0           0 11.352     NA      NA

Step 2.0: extract features

##                       label step_major step_minor label_minor    bgn
## 3          extract.features          2          0           0 11.352
## 4 extract.features.datetime          2          1           1 11.368
##      end elapsed
## 3 11.368   0.016
## 4     NA      NA

Step 2.1: extract features datetime

##                           label step_major step_minor label_minor    bgn
## 1 extract.features.datetime.bgn          1          0           0 11.389
##   end elapsed
## 1  NA      NA
## Warning in rm(pltObsAll): object 'pltObsAll' not found
##                       label step_major step_minor label_minor    bgn
## 4 extract.features.datetime          2          1           1 11.368
## 5    extract.features.image          2          2           2 11.399
##      end elapsed
## 4 11.398    0.03
## 5     NA      NA

Step 2.2: extract features image

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

##                        label step_major step_minor label_minor    bgn end
## 1 extract.features.image.bgn          1          0           0 11.426  NA
##   elapsed
## 1      NA
##                        label step_major step_minor label_minor    bgn
## 1 extract.features.image.bgn          1          0           0 11.426
## 2 extract.features.image.end          2          0           0 11.432
##      end elapsed
## 1 11.432   0.006
## 2     NA      NA
##                        label step_major step_minor label_minor    bgn
## 1 extract.features.image.bgn          1          0           0 11.426
## 2 extract.features.image.end          2          0           0 11.432
##      end elapsed
## 1 11.432   0.006
## 2     NA      NA
##                    label step_major step_minor label_minor    bgn   end
## 5 extract.features.image          2          2           2 11.399 11.44
## 6 extract.features.price          2          3           3 11.440    NA
##   elapsed
## 5   0.041
## 6      NA

Step 2.3: extract features price

##                        label step_major step_minor label_minor   bgn end
## 1 extract.features.price.bgn          1          0           0 11.46  NA
##   elapsed
## 1      NA
##                    label step_major step_minor label_minor    bgn    end
## 6 extract.features.price          2          3           3 11.440 11.466
## 7  extract.features.text          2          4           4 11.467     NA
##   elapsed
## 6   0.026
## 7      NA

Step 2.4: extract features text

##                       label step_major step_minor label_minor    bgn end
## 1 extract.features.text.bgn          1          0           0 11.501  NA
##   elapsed
## 1      NA
## Warning in rm(tmp_allobs_df): object 'tmp_allobs_df' not found
## Warning in rm(tmp_trnobs_df): object 'tmp_trnobs_df' not found
##                     label step_major step_minor label_minor    bgn    end
## 7   extract.features.text          2          4           4 11.467 11.511
## 8 extract.features.string          2          5           5 11.512     NA
##   elapsed
## 7   0.045
## 8      NA

Step 2.5: extract features string

##                         label step_major step_minor label_minor   bgn end
## 1 extract.features.string.bgn          1          0           0 11.54  NA
##   elapsed
## 1      NA
##                                       label step_major step_minor
## 1               extract.features.string.bgn          1          0
## 2 extract.features.stringfactorize.str.vars          2          0
##   label_minor    bgn    end elapsed
## 1           0 11.540 11.548   0.008
## 2           0 11.549     NA      NA
##            Gender            Income   HouseholdStatus    EducationLevel 
##          "Gender"          "Income" "HouseholdStatus"  "EducationLevel" 
##             Party           Q124742           Q124122           Q123464 
##           "Party"         "Q124742"         "Q124122"         "Q123464" 
##           Q123621           Q122769           Q122770           Q122771 
##         "Q123621"         "Q122769"         "Q122770"         "Q122771" 
##           Q122120           Q121699           Q121700           Q120978 
##         "Q122120"         "Q121699"         "Q121700"         "Q120978" 
##           Q121011           Q120379           Q120650           Q120472 
##         "Q121011"         "Q120379"         "Q120650"         "Q120472" 
##           Q120194           Q120012           Q120014           Q119334 
##         "Q120194"         "Q120012"         "Q120014"         "Q119334" 
##           Q119851           Q119650           Q118892           Q118117 
##         "Q119851"         "Q119650"         "Q118892"         "Q118117" 
##           Q118232           Q118233           Q118237           Q117186 
##         "Q118232"         "Q118233"         "Q118237"         "Q117186" 
##           Q117193           Q116797           Q116881           Q116953 
##         "Q117193"         "Q116797"         "Q116881"         "Q116953" 
##           Q116601           Q116441           Q116448           Q116197 
##         "Q116601"         "Q116441"         "Q116448"         "Q116197" 
##           Q115602           Q115777           Q115610           Q115611 
##         "Q115602"         "Q115777"         "Q115610"         "Q115611" 
##           Q115899           Q115390           Q114961           Q114748 
##         "Q115899"         "Q115390"         "Q114961"         "Q114748" 
##           Q115195           Q114517           Q114386           Q113992 
##         "Q115195"         "Q114517"         "Q114386"         "Q113992" 
##           Q114152           Q113583           Q113584           Q113181 
##         "Q114152"         "Q113583"         "Q113584"         "Q113181" 
##           Q112478           Q112512           Q112270           Q111848 
##         "Q112478"         "Q112512"         "Q112270"         "Q111848" 
##           Q111580           Q111220           Q110740           Q109367 
##         "Q111580"         "Q111220"         "Q110740"         "Q109367" 
##           Q108950           Q109244           Q108855           Q108617 
##         "Q108950"         "Q109244"         "Q108855"         "Q108617" 
##           Q108856           Q108754           Q108342           Q108343 
##         "Q108856"         "Q108754"         "Q108342"         "Q108343" 
##           Q107869           Q107491           Q106993           Q106997 
##         "Q107869"         "Q107491"         "Q106993"         "Q106997" 
##           Q106272           Q106388           Q106389           Q106042 
##         "Q106272"         "Q106388"         "Q106389"         "Q106042" 
##           Q105840           Q105655           Q104996           Q103293 
##         "Q105840"         "Q105655"         "Q104996"         "Q103293" 
##           Q102906           Q102674           Q102687           Q102289 
##         "Q102906"         "Q102674"         "Q102687"         "Q102289" 
##           Q102089           Q101162           Q101163           Q101596 
##         "Q102089"         "Q101162"         "Q101163"         "Q101596" 
##           Q100689           Q100680           Q100562            Q99982 
##         "Q100689"         "Q100680"         "Q100562"          "Q99982" 
##           Q100010            Q99716            Q99581            Q99480 
##         "Q100010"          "Q99716"          "Q99581"          "Q99480" 
##            Q98869            Q98578            Q98059            Q98078 
##          "Q98869"          "Q98578"          "Q98059"          "Q98078" 
##            Q98197            Q96024              .src 
##          "Q98197"          "Q96024"            ".src"
##                     label step_major step_minor label_minor    bgn    end
## 8 extract.features.string          2          5           5 11.512 11.568
## 9    extract.features.end          2          6           6 11.569     NA
##   elapsed
## 8   0.056
## 9      NA

Step 2.6: extract features end

## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0

##                   label step_major step_minor label_minor    bgn    end
## 9  extract.features.end          2          6           6 11.569 12.244
## 10  manage.missing.data          3          0           0 12.245     NA
##    elapsed
## 9    0.675
## 10      NA

Step 3.0: manage missing data

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        128        622 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         136 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              46             445             177             410 
##           Party         Q124742         Q124122         Q123464 
##              NA            1438             823             708 
##         Q123621         Q122769         Q122770         Q122771 
##             778             644             594             587 
##         Q122120         Q121699         Q121700         Q120978 
##             585             547             563             599 
##         Q121011         Q120379         Q120650         Q120472 
##             571             607             655             649 
##         Q120194         Q120012         Q120014         Q119334 
##             654             591             641             568 
##         Q119851         Q119650         Q118892         Q118117 
##             540             578             486             479 
##         Q118232         Q118233         Q118237         Q117186 
##             701             554             539             648 
##         Q117193         Q116797         Q116881         Q116953 
##             655             590             635             616 
##         Q116601         Q116441         Q116448         Q116197 
##             534             541             560             551 
##         Q115602         Q115777         Q115610         Q115611 
##             539             578             537             487 
##         Q115899         Q115390         Q114961         Q114748 
##             573             619             538             447 
##         Q115195         Q114517         Q114386         Q113992 
##             525             481             521             447 
##         Q114152         Q113583         Q113584         Q113181 
##             537             514             512             453 
##         Q112478         Q112512         Q112270         Q111848 
##             494             460             521             398 
##         Q111580         Q111220         Q110740         Q109367 
##             474             379             357             168 
##         Q108950         Q109244         Q108855         Q108617 
##             204               0             438             288 
##         Q108856         Q108754         Q108342         Q108343 
##             436             338             341             333 
##         Q107869         Q107491         Q106993         Q106997 
##             389             366             389             396 
##         Q106272         Q106388         Q106389         Q106042 
##             426             476             495             451 
##         Q105840         Q105655         Q104996         Q103293 
##             487             393             400             431 
##         Q102906         Q102674         Q102687         Q102289 
##             493             511             475             484 
##         Q102089         Q101162         Q101163         Q101596 
##             462             498             572             477 
##         Q100689         Q100680         Q100562          Q99982 
##             414             497             487             514 
##         Q100010          Q99716          Q99581          Q99480 
##             445             500             466             478 
##          Q98869          Q98578          Q98059          Q98078 
##             564             542             450             569 
##          Q98197          Q96024 
##             528             550
## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        128        622 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         136 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              46             445             177             410 
##           Party         Q124742         Q124122         Q123464 
##              NA            1438             823             708 
##         Q123621         Q122769         Q122770         Q122771 
##             778             644             594             587 
##         Q122120         Q121699         Q121700         Q120978 
##             585             547             563             599 
##         Q121011         Q120379         Q120650         Q120472 
##             571             607             655             649 
##         Q120194         Q120012         Q120014         Q119334 
##             654             591             641             568 
##         Q119851         Q119650         Q118892         Q118117 
##             540             578             486             479 
##         Q118232         Q118233         Q118237         Q117186 
##             701             554             539             648 
##         Q117193         Q116797         Q116881         Q116953 
##             655             590             635             616 
##         Q116601         Q116441         Q116448         Q116197 
##             534             541             560             551 
##         Q115602         Q115777         Q115610         Q115611 
##             539             578             537             487 
##         Q115899         Q115390         Q114961         Q114748 
##             573             619             538             447 
##         Q115195         Q114517         Q114386         Q113992 
##             525             481             521             447 
##         Q114152         Q113583         Q113584         Q113181 
##             537             514             512             453 
##         Q112478         Q112512         Q112270         Q111848 
##             494             460             521             398 
##         Q111580         Q111220         Q110740         Q109367 
##             474             379             357             168 
##         Q108950         Q109244         Q108855         Q108617 
##             204               0             438             288 
##         Q108856         Q108754         Q108342         Q108343 
##             436             338             341             333 
##         Q107869         Q107491         Q106993         Q106997 
##             389             366             389             396 
##         Q106272         Q106388         Q106389         Q106042 
##             426             476             495             451 
##         Q105840         Q105655         Q104996         Q103293 
##             487             393             400             431 
##         Q102906         Q102674         Q102687         Q102289 
##             493             511             475             484 
##         Q102089         Q101162         Q101163         Q101596 
##             462             498             572             477 
##         Q100689         Q100680         Q100562          Q99982 
##             414             497             487             514 
##         Q100010          Q99716          Q99581          Q99480 
##             445             500             466             478 
##          Q98869          Q98578          Q98059          Q98078 
##             564             542             450             569 
##          Q98197          Q96024 
##             528             550
##                  label step_major step_minor label_minor    bgn    end
## 10 manage.missing.data          3          0           0 12.245 12.802
## 11        cluster.data          4          0           0 12.802     NA
##    elapsed
## 10   0.557
## 11      NA

Step 4.0: cluster data

```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## Loading required package: proxy
## 
## Attaching package: 'proxy'
## The following objects are masked from 'package:stats':
## 
##     as.dist, dist
## The following object is masked from 'package:base':
## 
##     as.matrix
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr
## Loading required package: ggdendro
## [1] "Clustering features: "
## Warning in cor(data.matrix(glbObsAll[glbObsAll$.src == "Train",
## glbFeatsCluster]), : the standard deviation is zero
##               abs.cor.y
## Q108855.fctr 0.05525571
## Q116881.fctr 0.05625959
## Q98197.fctr  0.07400689
## Q113181.fctr 0.09842608
## Q115611.fctr 0.10612270
## [1] "    .rnorm cor: -0.0049"
## [1] "  Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.6810"
## Loading required package: lazyeval
##   Hhold.fctr .clusterid Hhold.fctr.clusterid   D   R  .entropy .knt
## 1          N          1                  N_1  59  85 0.6767573  144
## 2        MKn          1                MKn_1 123 178 0.6763589  301
## 3        MKy          1                MKy_1 306 530 0.6568082  836
## 4        PKn          1                PKn_1  40  23 0.6562848   63
## 5        PKy          1                PKy_1  13   7 0.6474466   20
## 6        SKn          1                SKn_1 454 557 0.6879485 1011
## 7        SKy          1                SKy_1  43  41 0.6928637   84
## [1] "glbObsAll$Hhold.fctr Entropy: 0.6743 (99.0230 pct)"
## [1] "Category: N"
## [1] "max distance(0.9762) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1315    1632          D          N           No           No           No
## 3744    4667          R          N           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1315           No           Pc           NA           NA           NA
## 3744           NA           NA           NA           NA          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1315           No          Yes          Yes           NA           NA
## 3744           NA           NA           NA          Yes           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1315           NA           No           NA           No          Yes
## 3744      Science           NA           NA           NA          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1315           No    Receiving           NA          Yes          Yes
## 3744          Yes           NA           NA           NA           No
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1315           No           NA          Yes    Odd hours           NA
## 3744           NA           NA          Yes           NA   Hot headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1315           NA           NA          Yes           No           NA
## 3744           NA        Right           No           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1315           No           NA           NA           NA           NA
## 3744           NA         P.M.           NA        Start           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1315           NA           NA           NA          Yes           NA
## 3744           NA           NA          Yes           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1315           NA           NA           NA           NA           NA
## 3744           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1315        Tunes       People           NA          Yes           NA
## 3744           NA           NA           NA          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1315          Yes          Yes   Supportive           No           NA
## 3744          Yes           NA           NA           No           PC
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1315           No           No     Cautious           NA           NA
## 3744           NA           No           NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1315           NA           No    In-person           NA           No
## 3744           NA           NA    In-person           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1315          Yes           NA           NA           NA           NA
## 3744           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1315           NA           No           NA          Yes           No
## 3744           NA           NA           NA          Yes           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1315           No           No           No          Yes           No
## 3744           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1315           NA     Optimist           NA           No           No
## 3744           NA     Optimist          Mom           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1315           No           NA           No          NA          No
## 3744           No          Yes           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1315          No         Yes          NA         Yes          NA
## 3744          NA          NA         Yes         Yes          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1315         Yes          NA          NA
## 3744          NA          NA          NA
## [1] "min distance(0.9567) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4256    5305          R          N           NA           NA           NA
## 4775    5956          R          N           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4256           NA           NA           NA           NA           NA
## 4775           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4256           NA           NA           NA           NA           NA
## 4775           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4256           NA           NA           NA           NA           NA
## 4775           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4256           NA           NA           NA           NA           NA
## 4775           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 4256           NA           NA           NA             NA           NA
## 4775           NA           NA           NA Standard hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4256           NA           NA           NA           No           No
## 4775           No        Right          Yes          Yes           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4256          Yes         P.M.          Yes          End          Yes
## 4775           No         P.M.          Yes        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4256          Yes           Me          Yes          Yes          Yes
## 4775          Yes           Cs           No           No           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4256          Yes           No          TMI           No           NA
## 4775          Yes           No   Mysterious          Yes           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4256           NA           NA          Yes           NA           NA
## 4775        Tunes       People          Yes          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4256           NA           NA           NA           No          Mac
## 4775          Yes          Yes   Supportive           No          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 4256          Yes           No Risk-friendly       Umm...           No
## 4775           No           No      Cautious         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4256    Socialize          Yes           NA           NA           No
## 4775        Space           No    In-person           No           No
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4256          Yes           No           Yy          Yes           No
## 4775          Yes          Yes           Yy          Yes           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4256           No           No           No          Yes          Yes
## 4775           No          Yes           No          Yes          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4256          Yes          Yes           No          Yes           No
## 4775          Yes           No           No          Yes           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4256          Own     Optimist          Dad           No           No
## 4775          Own     Optimist          Dad          Yes           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4256          Yes          Yes           No        Nope          NA
## 4775           No          Yes           No        Nope          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4256          NA          NA         Yes         Yes         Yes
## 4775         Yes         Yes         Yes         Yes         Yes
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4256         Yes         Yes          NA
## 4775         Yes         Yes          NA
## [1] "Category: MKn"
## [1] "max distance(0.9764) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2961    3680          R        MKn           NA           NA          Yes
## 4750    5926          R        MKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2961           No           Pt          Yes           No           No
## 4750           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2961           No          Yes          Yes          Yes          Yes
## 4750           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2961      Science          Yes           NA          Yes          Yes
## 4750           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2961           No       Giving           No          Yes          Yes
## 4750           NA           NA           NA           NA          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 2961           No           Pr           No Standard hours   Hot headed
## 4750          Yes           Pr          Yes             NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2961           NA           NA           NA           NA           NA
## 4750           NA           NA           NA          Yes          Yes
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2961           NA           NA           NA        Start           NA
## 4750           No           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2961           NA           NA           NA           NA           NA
## 4750           NA           NA           NA          Yes          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2961           NA           NA           NA           NA           NA
## 4750          Yes          Yes   Mysterious           NA           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2961           NA           NA           NA           NA           NA
## 4750        Tunes   Technology           NA           No           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2961           NA           NA           NA           NA           NA
## 4750           No          Yes   Supportive           No           PC
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2961           No           No     Cautious       Umm...           No
## 4750          Yes           No     Cautious       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2961        Space          Yes       Online           No           No
## 4750        Space           No    In-person           No           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2961          Yes          Yes           Gr           NA           NA
## 4750           No           No           Gr          Yes          Yes
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2961           NA           NA           NA           NA           NA
## 4750          Yes           No           No          Yes          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2961           NA           NA           NA           NA           NA
## 4750           No           No          Yes          Yes          Yes
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2961           NA           NA           NA           NA           NA
## 4750          Own     Optimist          Dad          Yes           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2961           NA           NA           NA          NA          NA
## 4750          Yes          Yes          Yes        Nope          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2961          NA          NA          NA          NA          NA
## 4750          No         Yes         Yes         Yes         Yes
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2961          NA          NA          NA
## 4750         Yes         Yes          No
## [1] "min distance(0.9562) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4260    5310          R        MKn           NA           NA           NA
## 6399    4226       <NA>        MKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4260           NA           Pt          Yes           NA           NA
## 6399           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4260           NA           NA          Yes          Yes           NA
## 6399           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4260           NA           NA           NA           NA           NA
## 6399           NA           No    Try first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4260           NA           NA           NA           NA           NA
## 6399          Yes       Giving           No           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 4260           NA           NA           NA Standard hours  Cool headed
## 6399           NA           NA          Yes Standard hours           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4260           No        Right           No          Yes           No
## 6399           No        Right          Yes          Yes           No
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4260          Yes         P.M.           No        Start          Yes
## 6399          Yes         A.M.           NA          End           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4260          Yes           Me           No          Yes          Yes
## 6399          Yes           Me           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4260           NA           NA           NA           NA           No
## 6399          Yes          Yes   Mysterious           No           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4260         Talk       People          Yes           NA          Yes
## 6399         Talk   Technology          Yes           No          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4260           NA           No           NA           NA           PC
## 6399           No           NA           NA           No           PC
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 4260          Yes           No Risk-friendly         Yes!           No
## 6399          Yes           No      Cautious           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4260        Space          Yes           NA           NA           NA
## 6399           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4260           NA           NA           NA           NA           NA
## 6399           NA          Yes           Gr           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4260           NA           NA           NA           NA           NA
## 6399           NA           NA           NA           NA           No
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4260           NA           NA           NA           NA           NA
## 6399           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4260         Rent     Optimist          Mom          Yes           NA
## 6399          Own     Optimist          Dad           No          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4260           NA           NA           NA          NA          No
## 6399           NA           NA           NA          NA          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4260          No         Yes          NA          NA          NA
## 6399         Yes          No         Yes          No          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4260          NA          NA         Yes
## 6399          NA          NA          NA
## [1] "Category: MKy"
## [1] "max distance(0.9760) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1198    1481          R        MKy           NA           NA           NA
## 4133    5153          D        MKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1198           NA           NA           NA           NA           NA
## 4133           NA           Pc          Yes           No          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1198           No          Yes          Yes          Yes          Yes
## 4133          Yes          Yes          Yes           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1198           NA           No           NA           NA           NA
## 4133          Art          Yes           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1198           NA           NA           No           No           NA
## 4133           NA           NA           No          Yes          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1198           NA           NA           NA           NA           NA
## 4133           No           Id          Yes    Odd hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1198           NA           NA           NA           NA           NA
## 4133           No        Happy           No           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1198           NA           NA           NA           NA           NA
## 4133           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1198           NA           NA           NA           NA           NA
## 4133           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1198           NA           NA           NA           NA           NA
## 4133           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1198           NA           NA           NA           NA           NA
## 4133           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1198           NA           NA           NA           NA           NA
## 4133          Yes           No    Demanding          Yes          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 1198           NA           No      Cautious           NA           NA
## 4133          Yes           No Risk-friendly         Yes!          Yes
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1198           NA           NA           NA           NA           No
## 4133        Space           No       Online          Yes          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1198          Yes          Yes           Gr           No           No
## 4133          Yes           No           Yy           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1198           No           No          Yes           No           NA
## 4133           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1198           NA           NA          Yes           No           No
## 4133           No           No          Yes           No          Yes
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1198          Own     Optimist          Mom          Yes          Yes
## 4133         Rent           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1198          Yes          Yes          Yes        Nope          No
## 4133           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1198          No          No          NA          NA          NA
## 4133          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1198         Yes          No          No
## 4133          NA          NA          NA
## [1] "min distance(0.9501) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4299    5365          R        MKy           NA           NA           NA
## 6802    6188       <NA>        MKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4299           NA           NA          Yes           NA          Yes
## 6802           NA           NA          Yes          End          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4299          Yes           NA          Yes          Yes           No
## 6802          Yes           NA          Yes          Yes           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4299           No          Yes   Mysterious          Yes           No
## 6802           No           No   Mysterious           No          Yes
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4299         Talk       People          Yes           NA           NA
## 6802           NA   Technology           No           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 4299          Yes           No            NA           NA           NA
## 6802          Yes           No Risk-friendly       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4299           NA           NA           NA           NA           NA
## 6802        Space          Yes    In-person          Yes           No
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4299           NA           NA           NA           NA           NA
## 6802          Yes          Yes           Yy           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4299           NA           NA           NA           NA           NA
## 6802           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4299           NA           NA           NA          NA          NA
## 6802           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4299          NA          NA          NA          NA          NA
## 6802          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4299          NA          NA          NA
## 6802          NA          NA          NA
## [1] "Category: PKn"
## [1] "max distance(0.9748) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 4183    5219          D        PKn           NA           NA           NA
## 6176    3074       <NA>        PKn           No          Yes          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 4183           NA           NA           NA           NA           NA
## 6176           No           Pc          Yes           No           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 4183           NA           NA           NA           NA           NA
## 6176           No          Yes           No           No          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 4183           NA           NA           NA           NA           NA
## 6176      Science           No  Study first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 4183           NA           NA           NA           NA           NA
## 6176          Yes       Giving           No          Yes           No
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 4183           NA           NA           NA             NA           NA
## 6176           No           Pr           No Standard hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 4183           NA           NA           NA           NA           NA
## 6176          Yes        Right           No          Yes          Yes
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 4183           NA           NA           NA           NA           NA
## 6176           No           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 4183           NA           NA           NA           NA           NA
## 6176           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 4183          Yes           No          TMI           No          Yes
## 6176           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 4183           NA           NA           NA           NA           NA
## 6176           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 4183           NA           NA    Demanding           No          Mac
## 6176           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 4183          Yes           No     Cautious       Umm...           No
## 6176          Yes           No     Cautious       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 4183    Socialize           No    In-person          Yes          Yes
## 6176        Space          Yes    In-person           No          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 4183          Yes          Yes           NA           No           No
## 6176          Yes          Yes           Gr          Yes           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 4183           No           No          Yes           No           No
## 6176          Yes           No           No          Yes           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 4183          Yes          Yes           NA           NA           No
## 6176           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 4183         Rent     Optimist          Mom          Yes          Yes
## 6176           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 4183          Yes          Yes          Yes        Nope          No
## 6176           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 4183          No         Yes         Yes          No          No
## 6176          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 4183         Yes          No          No
## 6176          NA          NA          NA
## [1] "min distance(0.9601) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 3970    4947          D        PKn          Yes          Yes          Yes
## 6428    4362       <NA>        PKn          Yes          Yes          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 3970           No           Pc          Yes          Yes           No
## 6428           No           Pc          Yes          Yes           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 3970           No          Yes           No          Yes          Yes
## 6428           No          Yes           No          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 3970      Science           No    Try first           No          Yes
## 6428      Science          Yes  Study first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 3970          Yes       Giving           No          Yes           No
## 6428          Yes       Giving          Yes          Yes           No
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 3970           No           Pr           No      Odd hours  Cool headed
## 6428          Yes           Id          Yes Standard hours   Hot headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 3970           No        Happy           No          Yes           No
## 6428          Yes        Happy           No          Yes          Yes
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 3970          Yes         A.M.          Yes          End          Yes
## 6428          Yes         A.M.          Yes        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 3970          Yes           Cs           No           No           No
## 6428          Yes           Cs          Yes          Yes          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 3970          Yes           No          TMI           No           No
## 6428          Yes          Yes   Mysterious          Yes           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 3970        Tunes       People          Yes           No          Yes
## 6428         Talk       People          Yes          Yes           No
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 3970          Yes          Yes    Demanding           No           PC
## 6428          Yes          Yes   Supportive           No           PC
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 3970          Yes           No     Cautious       Umm...           No
## 6428           No           No     Cautious         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 3970        Space           No    In-person           No          Yes
## 6428        Space          Yes    In-person           No          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 3970          Yes          Yes           Gr          Yes           No
## 6428          Yes          Yes           Gr          Yes           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 3970           No          Yes          Yes           No          Yes
## 6428          Yes          Yes           No           No          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 3970          Yes          Yes          Yes           No           No
## 6428          Yes          Yes           No          Yes           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 3970          Own     Optimist          Dad           No          Yes
## 6428          Own     Optimist          Dad           No           No
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 3970          Yes          Yes          Yes        Nope          No
## 6428          Yes          Yes          Yes      Check!          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 3970          No         Yes         Yes          No         Yes
## 6428          No         Yes         Yes          NA         Yes
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 3970         Yes          No          No
## 6428         Yes          No         Yes
## [1] "Category: PKy"
## [1] "max distance(0.9743) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1564    1937          R        PKy           NA           NA           NA
## 4351    5432          D        PKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1564           NA           NA           NA           NA           NA
## 4351           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1564           NA           NA           NA           NA           NA
## 4351           NA           NA           No          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1564           NA           NA           NA           NA           NA
## 4351      Science           No    Try first           No          Yes
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1564           No       Giving           No          Yes          Yes
## 4351          Yes       Giving           No          Yes          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1564           No           Id           No    Odd hours  Cool headed
## 4351           No           Id          Yes           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1564           No        Happy          Yes          Yes          Yes
## 4351           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1564          Yes           NA           NA           NA           NA
## 4351           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1564           NA           NA           NA           NA           NA
## 4351           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1564           NA           NA           NA           NA           NA
## 4351           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1564           NA           NA           NA           NA           NA
## 4351           NA           NA           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1564           NA          Yes    Demanding          Yes           PC
## 4351           NA           NA           NA           NA           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1564          Yes           No           NA           NA           NA
## 4351           No           No     Cautious         Yes!          Yes
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1564           NA           NA           NA           NA           NA
## 4351        Space           No           NA           NA           No
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1564          Yes           No           Gr          Yes           No
## 4351          Yes           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1564           No           No           No           No          Yes
## 4351           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1564          Yes           No           NA           NA           NA
## 4351           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1564          Own     Optimist          Dad          Yes           No
## 4351           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1564          Yes          Yes           No        Nope          No
## 4351           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1564          No          NA          No         Yes          No
## 4351          No         Yes          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1564         Yes          No         Yes
## 4351          NA          NA          NA
## [1] "min distance(0.9619) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 719      891          D        PKy          Yes           No          Yes
## 1417    1762          D        PKy          Yes           No          Yes
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 719            No           Pt           No           No           No
## 1417           No           Pt           No          Yes           No
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 719            No          Yes           No          Yes          Yes
## 1417           No          Yes          Yes          Yes          Yes
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 719       Science           No  Study first          Yes           No
## 1417      Science           No  Study first          Yes           No
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 719            No       Giving           No           No          Yes
## 1417           No       Giving          Yes           No          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 719           Yes           Id           No    Odd hours   Hot headed
## 1417          Yes           Pr          Yes    Odd hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 719            No        Happy           No           No          Yes
## 1417          Yes        Happy          Yes          Yes          Yes
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 719            No         P.M.          Yes        Start           No
## 1417           No         A.M.          Yes        Start          Yes
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 719           Yes           Cs          Yes          Yes           No
## 1417          Yes           Me          Yes           No           No
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 719            No          Yes   Mysterious           No          Yes
## 1417          Yes           No   Mysterious          Yes          Yes
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 719         Tunes       People           No           No          Yes
## 1417         Talk   Technology          Yes          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 719           Yes           No   Supportive           No          Mac
## 1417          Yes           No    Demanding           No           PC
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 719           Yes           No Risk-friendly         Yes!           No
## 1417          Yes           No Risk-friendly       Umm...           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 719     Socialize          Yes    In-person           No          Yes
## 1417    Socialize          Yes    In-person           No          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 719           Yes          Yes           Gr          Yes          Yes
## 1417          Yes           No           Yy           No          Yes
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 719            No           No           No          Yes          Yes
## 1417          Yes          Yes          Yes           No          Yes
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 719           Yes           No          Yes          Yes           No
## 1417           No           No          Yes          Yes          Yes
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 719           Own     Optimist          Mom           No           No
## 1417          Own     Optimist          Mom           No          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 719            No          Yes          Yes        Nope          No
## 1417          Yes          Yes          Yes      Check!          No
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 719           No          No         Yes          No          No
## 1417          No         Yes         Yes          No         Yes
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 719   Only-child          No          No
## 1417         Yes          No          No
## [1] "Category: SKn"
## [1] "max distance(0.9768) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2395    2988          R        SKn           NA           NA           NA
## 6279    3607       <NA>        SKn           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2395           NA           Pc          Yes          Yes          Yes
## 6279           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2395          Yes          Yes          Yes           No          Yes
## 6279           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2395          Art           No           NA          Yes           No
## 6279           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2395          Yes       Giving           NA          Yes           NA
## 6279           NA           NA           No          Yes           No
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 2395           NA           NA           NA             NA           NA
## 6279           No           Pr          Yes Standard hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2395           NA           NA           NA           NA           NA
## 6279           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2395           NA           NA           NA           NA           NA
## 6279           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2395           NA           NA           NA           NA           NA
## 6279           NA           NA           NA           NA          Yes
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2395           NA           NA           NA           NA           NA
## 6279          Yes           No   Mysterious          Yes           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2395           NA           NA           NA           NA           NA
## 6279         Talk   Technology           NA           NA           NA
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2395           NA           NA           NA           NA           NA
## 6279           NA          Yes           NA           No           NA
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 2395           NA           No           NA           NA           NA
## 6279          Yes           No     Cautious         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2395           NA           NA           NA           NA           NA
## 6279        Space           No    In-person           No           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2395          Yes           NA           NA           NA           NA
## 6279           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2395           NA           NA           NA           NA           No
## 6279           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2395           NA           NA           NA           NA           NA
## 6279           NA           NA           No          Yes           No
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2395           NA           NA           NA           NA           NA
## 6279          Own     Optimist          Dad           No          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2395           NA           NA           NA          NA          NA
## 6279           No          Yes          Yes      Check!         Yes
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2395         Yes         Yes         Yes         Yes          NA
## 6279          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2395         Yes         Yes         Yes
## 6279          NA          NA          NA
## [1] "min distance(0.9542) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2674    3327          R        SKn           NA           NA           NA
## 4650    5804          R        SKn           NA          Yes           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2674           NA           NA           NA           NA           NA
## 4650           NA           Pc           NA           NA          Yes
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2674           NA           NA           NA           NA           NA
## 4650          Yes           No           NA           NA           No
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2674           NA           NA           NA           NA           NA
## 4650           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2674           NA           NA           NA           NA           NA
## 4650           NA       Giving          Yes          Yes          Yes
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 2674           NA           NA           NA             NA           NA
## 4650           No           NA          Yes Standard hours           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2674           NA           NA           NA           NA           No
## 4650           No        Happy          Yes           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2674           No         P.M.          Yes        Start          Yes
## 4650          Yes           NA          Yes        Start           No
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2674          Yes           Me           No          Yes          Yes
## 4650          Yes           Cs           No          Yes           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2674           No           No           NA           No          Yes
## 4650           No           No   Mysterious           NA           No
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2674        Tunes   Technology          Yes           No           No
## 4650        Tunes       People          Yes           No          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2674          Yes           No           NA           NA           PC
## 4650           No           No   Supportive           No          Mac
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 2674           No           No Risk-friendly       Umm...           No
## 4650           No           No      Cautious         Yes!           No
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2674    Socialize           No    In-person          Yes           No
## 4650        Space           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2674          Yes          Yes           Yy           No          Yes
## 4650           NA           NA           NA           NA           No
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2674           No           No           No           NA           NA
## 4650           No           NA          Yes           No           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2674           NA           NA           NA           NA           NA
## 4650           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2674           NA           NA           NA           NA           NA
## 4650           NA    Pessimist           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2674           NA           NA           NA          NA          No
## 4650          Yes          Yes           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2674          No         Yes         Yes          No         Yes
## 4650          NA          NA          NA         Yes         Yes
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2674         Yes          NA         Yes
## 4650         Yes          NA          No
## [1] "Category: SKy"
## [1] "max distance(0.9760) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 2360    2939          D        SKy           NA           NA           NA
## 3852    4799          R        SKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 2360           NA           Pc          Yes           No           No
## 3852           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 2360           No          Yes           NA           NA          Yes
## 3852           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 2360      Science          Yes    Try first           No           No
## 3852           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 2360          Yes       Giving           NA           No          Yes
## 3852           NA           NA          Yes           No           No
##      Q118233.fctr Q118232.fctr Q118117.fctr   Q117193.fctr Q117186.fctr
## 2360          Yes           NA           NA Standard hours  Cool headed
## 3852          Yes           NA           No      Odd hours  Cool headed
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 2360           No        Happy          Yes          Yes          Yes
## 3852           No        Happy           No          Yes          Yes
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 2360           No           NA           NA           NA           NA
## 3852           No           NA           NA        Start           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 2360           NA           NA           NA           NA           NA
## 3852           NA           Me           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 2360           NA           NA           NA           NA           NA
## 3852          Yes           No   Mysterious           No          Yes
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 2360           NA           NA           NA           NA           NA
## 3852        Tunes   Technology           NA           No          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 2360           NA           NA           NA           NA           NA
## 3852          Yes           No   Supportive           No           PC
##      Q109367.fctr Q109244.fctr  Q108950.fctr Q108855.fctr Q108617.fctr
## 2360          Yes           No Risk-friendly         Yes!           NA
## 3852          Yes           No            NA           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 2360    Socialize          Yes       Online          Yes           No
## 3852           NA           NA    In-person           No          Yes
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 2360          Yes           No           Yy           NA           NA
## 3852          Yes          Yes           Gr          Yes          Yes
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 2360           NA           NA           NA           NA           NA
## 3852          Yes           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 2360           NA           NA           NA           NA           NA
## 3852           NA           NA           NA           NA          Yes
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 2360           NA           NA           NA           NA           NA
## 3852          Own           NA           NA           NA          Yes
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 2360           NA           NA           NA          NA          NA
## 3852           No           No          Yes      Check!         Yes
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 2360          NA          NA          NA          NA          No
## 3852          No         Yes         Yes          No          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 2360         Yes         Yes          NA
## 3852          NA          NA          NA
## [1] "min distance(0.9566) pair:"
##      USER_ID Party.fctr Hhold.fctr Q124742.fctr Q124122.fctr Q123621.fctr
## 1423    1771          D        SKy           NA           NA           NA
## 3974    4952          R        SKy           NA           NA           NA
##      Q123464.fctr Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q121700.fctr Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q120472.fctr Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q119851.fctr Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q118233.fctr Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q116797.fctr Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q116448.fctr Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr
## 1423           NA           NA          Yes        Start          Yes
## 3974           NA           NA           NA           NA           NA
##      Q115611.fctr Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr
## 1423           No           Cs          Yes          Yes           No
## 3974           NA           NA           NA           NA           NA
##      Q114748.fctr Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr
## 1423          Yes           No          TMI           NA          Yes
## 3974           NA           NA           NA           NA           NA
##      Q113583.fctr Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr
## 1423           NA           NA          Yes          Yes           NA
## 3974        Tunes       People          Yes          Yes          Yes
##      Q112270.fctr Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr
## 1423           No          Yes   Supportive           NA           NA
## 3974           No          Yes   Supportive          Yes           PC
##      Q109367.fctr Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr
## 1423          Yes           No           NA           NA           NA
## 3974          Yes           No     Cautious           NA           NA
##      Q108856.fctr Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q107491.fctr Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q106389.fctr Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q103293.fctr Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q102089.fctr Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr
## 1423           NA           NA           NA           NA           NA
## 3974           NA           NA           NA           NA           NA
##      Q100680.fctr Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr
## 1423           NA           NA           NA          NA          NA
## 3974           NA           NA           NA          NA          NA
##      Q99581.fctr Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr
## 1423          NA          NA          NA          NA          NA
## 3974          NA          NA          NA          NA          NA
##      Q98059.fctr Q98078.fctr Q96024.fctr
## 1423          NA          NA          NA
## 3974          NA          NA          NA
##    Hhold.fctr .clusterid Hhold.fctr.clusterid   D   R  .entropy .knt
## 1           N          1                  N_1  22  29 0.6836979   51
## 2           N          2                  N_2  17  23 0.6818546   40
## 3           N          3                  N_3  13  15 0.6905940   28
## 4           N          4                  N_4   7  18 0.5929533   25
## 5         MKn          1                MKn_1  52  47 0.6918713   99
## 6         MKn          2                MKn_2  25  58 0.6118817   83
## 7         MKn          3                MKn_3  25  39 0.6690268   64
## 8         MKn          4                MKn_4  21  34 0.6649472   55
## 9         MKy          1                MKy_1  97 178 0.6491186  275
## 10        MKy          2                MKy_2  97 107 0.6919452  204
## 11        MKy          3                MKy_3  56 126 0.6172418  182
## 12        MKy          4                MKy_4  56 119 0.6268695  175
## 13        PKn          1                PKn_1  24  15 0.6662784   39
## 14        PKn          2                PKn_2  13   5 0.5908422   18
## 15        PKn          3                PKn_3   3   3 0.6931472    6
## 16        PKy          1                PKy_1  10   3 0.5402041   13
## 17        PKy          2                PKy_2   2   0 0.0000000    2
## 18        PKy          3                PKy_3   1   2 0.6365142    3
## 19        PKy          4                PKy_4   0   2 0.0000000    2
## 20        SKn          1                SKn_1 250 225 0.6917615  475
## 21        SKn          2                SKn_2 116 227 0.6398292  343
## 22        SKn          3                SKn_3  88 105 0.6892628  193
## 23        SKy          1                SKy_1  16  20 0.6869616   36
## 24        SKy          2                SKy_2  17  14 0.6884572   31
## 25        SKy          3                SKy_3  10   7 0.6774944   17
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.6609 (98.0048 pct)"
##                      label step_major step_minor label_minor    bgn    end
## 11            cluster.data          4          0           0 12.802 61.839
## 12 partition.data.training          5          0           0 61.840     NA
##    elapsed
## 11  49.037
## 12      NA

Step 5.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
## Loading required package: reshape2
## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 6.26 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 6.26 secs"
## [1] "lclgetMatrixSimilarity: duration: 16.423000 secs"
## Loading required package: sampling
## 
## Attaching package: 'sampling'
## The following object is masked from 'package:caret':
## 
##     cluster
## [1] "lclgetMatrixSimilarity: duration: 10.897000 secs"
## [1] "lclgetMatrixSimilarity: duration: 3.835000 secs"
## [1] "lclgetMatrixSimilarity: duration: 9.292000 secs"

## [1] "Similarity of partitions:"
##         cor cosineSmy obs.x obs.y
## 1 0.9999879 0.9254892   OOB   Fit
## 2 0.9999877 0.9182758   OOB   New
## 3 0.9999877 0.9160134   Fit   New
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 48.06 secs"
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA      622
## Fit            824             1133       NA
## OOB            214              288       NA
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA        1
## Fit      0.4210526        0.5789474       NA
## OOB      0.4262948        0.5737052       NA
##   Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6        SKn    810    201    252    0.413898825     0.40039841
## 2        MKy    680    156    195    0.347470618     0.31075697
## 1        MKn    226     75     94    0.115482882     0.14940239
## 3          N    116     28     33    0.059274400     0.05577689
## 7        SKy     61     23     27    0.031170158     0.04581673
## 4        PKn     50     13     15    0.025549310     0.02589641
## 5        PKy     14      6      6    0.007153807     0.01195219
##   .freqRatio.Tst
## 6    0.405144695
## 2    0.313504823
## 1    0.151125402
## 3    0.053054662
## 7    0.043408360
## 4    0.024115756
## 5    0.009646302
## [1] "glbObsAll: "
## [1] 3081  222
## [1] "glbObsTrn: "
## [1] 2459  222
## [1] "glbObsFit: "
## [1] 1957  221
## [1] "glbObsOOB: "
## [1] 502 221
## [1] "glbObsNew: "
## [1] 622 221
## [1] "partition.data.training chunk: teardown: elapsed: 48.67 secs"
##                      label step_major step_minor label_minor     bgn
## 12 partition.data.training          5          0           0  61.840
## 13         select.features          6          0           0 110.583
##        end elapsed
## 12 110.582  48.743
## 13      NA      NA

Step 6.0: select features

```{r select.features, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero
##                         cor.y exclude.as.feat    cor.y.abs cor.high.X
## Q115611.fctr     0.1061227024               0 0.1061227024         NA
## Q113181.fctr     0.0984260825               0 0.0984260825         NA
## .clusterid       0.0937606364               1 0.0937606364         NA
## .clusterid.fctr  0.0937606364               0 0.0937606364         NA
## Q98197.fctr      0.0740068938               0 0.0740068938         NA
## Q116881.fctr     0.0562595864               0 0.0562595864         NA
## Q108855.fctr     0.0552557062               0 0.0552557062         NA
## Q106272.fctr     0.0508423733               0 0.0508423733         NA
## Q122771.fctr     0.0506936997               0 0.0506936997         NA
## Q123621.fctr     0.0496371444               0 0.0496371444         NA
## Q106388.fctr     0.0494091927               0 0.0494091927         NA
## Q110740.fctr     0.0475251274               0 0.0475251274         NA
## USER_ID          0.0451253514               1 0.0451253514         NA
## .pos             0.0449751220               1 0.0449751220         NA
## Q122769.fctr     0.0357238967               0 0.0357238967         NA
## Q120472.fctr     0.0353553175               0 0.0353553175         NA
## Q101596.fctr     0.0310707949               0 0.0310707949         NA
## Q119334.fctr     0.0307545744               0 0.0307545744         NA
## Q114152.fctr     0.0298031360               0 0.0298031360         NA
## Q98869.fctr      0.0279837842               0 0.0279837842         NA
## Q115899.fctr     0.0275355442               0 0.0275355442         NA
## Q116797.fctr     0.0264023055               0 0.0264023055         NA
## YOB.Age.dff      0.0263896332               0 0.0263896332         NA
## Q118232.fctr     0.0263557469               0 0.0263557469         NA
## Gender.fctr      0.0260785749               0 0.0260785749         NA
## Q105655.fctr     0.0254502518               0 0.0254502518         NA
## Q99480.fctr      0.0241968063               0 0.0241968063         NA
## Q123464.fctr     0.0232487747               0 0.0232487747         NA
## Q120650.fctr     0.0228127973               0 0.0228127973         NA
## Q122120.fctr     0.0226963845               0 0.0226963845         NA
## Q107869.fctr     0.0218682393               0 0.0218682393         NA
## Q120014.fctr     0.0202731591               0 0.0202731591         NA
## Q102289.fctr     0.0192468615               0 0.0192468615         NA
## Income.fctr      0.0179840418               0 0.0179840418         NA
## Q122770.fctr     0.0174735493               0 0.0174735493         NA
## Q111580.fctr     0.0170269417               0 0.0170269417         NA
## Q116601.fctr     0.0159184435               0 0.0159184435         NA
## Q117186.fctr     0.0158641235               0 0.0158641235         NA
## Q106993.fctr     0.0151551332               0 0.0151551332         NA
## Q112270.fctr     0.0147892685               0 0.0147892685         NA
## Q101162.fctr     0.0139326027               0 0.0139326027         NA
## Q108856.fctr     0.0128750759               0 0.0128750759         NA
## Q117193.fctr     0.0114974111               0 0.0114974111         NA
## Q116441.fctr     0.0093463969               0 0.0093463969         NA
## Q119851.fctr     0.0089549525               0 0.0089549525         NA
## Q111848.fctr     0.0085442819               0 0.0085442819         NA
## Q98578.fctr      0.0067135887               0 0.0067135887         NA
## Q118892.fctr     0.0063006467               0 0.0063006467         NA
## Q114386.fctr     0.0057240993               0 0.0057240993         NA
## Q120978.fctr     0.0055115231               0 0.0055115231         NA
## Q112512.fctr     0.0053167658               0 0.0053167658         NA
## Q102674.fctr     0.0050627208               0 0.0050627208         NA
## Q96024.fctr      0.0040534729               0 0.0040534729         NA
## Q108950.fctr     0.0039412433               0 0.0039412433         NA
## Q115610.fctr     0.0037395055               0 0.0037395055         NA
## YOB.Age.fctr     0.0031160191               0 0.0031160191         NA
## Q112478.fctr     0.0028932765               0 0.0028932765         NA
## Q116197.fctr     0.0026906806               0 0.0026906806         NA
## Q124742.fctr     0.0025316261               0 0.0025316261         NA
## Q106389.fctr     0.0020182255               0 0.0020182255         NA
## Edn.fctr         0.0013569584               0 0.0013569584         NA
## Q118117.fctr     0.0006385446               0 0.0006385446         NA
## Q100562.fctr     0.0001743827               0 0.0001743827         NA
## Q107491.fctr    -0.0001103153               0 0.0001103153         NA
## Q116448.fctr    -0.0023584430               0 0.0023584430         NA
## Q108754.fctr    -0.0027742157               0 0.0027742157         NA
## Q116953.fctr    -0.0029373549               0 0.0029373549         NA
## Q115602.fctr    -0.0031238519               0 0.0031238519         NA
## Q118233.fctr    -0.0033273008               0 0.0033273008         NA
## Q120012.fctr    -0.0039513241               0 0.0039513241         NA
## Q118237.fctr    -0.0043335513               0 0.0043335513         NA
## Q99581.fctr     -0.0046486977               0 0.0046486977         NA
## .rnorm          -0.0048723001               0 0.0048723001         NA
## Q120194.fctr    -0.0057263432               0 0.0057263432         NA
## Q115777.fctr    -0.0059804934               0 0.0059804934         NA
## Q106997.fctr    -0.0063914109               0 0.0063914109         NA
## Q100680.fctr    -0.0072431931               0 0.0072431931         NA
## Q113584.fctr    -0.0076436688               0 0.0076436688         NA
## Q108343.fctr    -0.0079333386               0 0.0079333386         NA
## Q121700.fctr    -0.0087942115               0 0.0087942115         NA
## Q105840.fctr    -0.0088034036               0 0.0088034036         NA
## Q120379.fctr    -0.0089842116               0 0.0089842116         NA
## Q103293.fctr    -0.0090167793               0 0.0090167793         NA
## Q124122.fctr    -0.0099503887               0 0.0099503887         NA
## Q109367.fctr    -0.0100116070               0 0.0100116070         NA
## Q113992.fctr    -0.0100378101               0 0.0100378101         NA
## Q121699.fctr    -0.0121369662               0 0.0121369662         NA
## Q121011.fctr    -0.0122186222               0 0.0122186222         NA
## Q114748.fctr    -0.0128363203               0 0.0128363203         NA
## Q106042.fctr    -0.0135167901               0 0.0135167901         NA
## Q111220.fctr    -0.0145971279               0 0.0145971279         NA
## Q114517.fctr    -0.0148538356               0 0.0148538356         NA
## YOB             -0.0169432580               1 0.0169432580         NA
## Q102687.fctr    -0.0169904229               0 0.0169904229         NA
## Q102906.fctr    -0.0173704239               0 0.0173704239         NA
## Q98078.fctr     -0.0177772661               0 0.0177772661         NA
## Q115390.fctr    -0.0196547694               0 0.0196547694         NA
## Q102089.fctr    -0.0200451075               0 0.0200451075         NA
## Q100010.fctr    -0.0208031518               0 0.0208031518         NA
## Q99982.fctr     -0.0208604939               0 0.0208604939         NA
## Q113583.fctr    -0.0211296876               0 0.0211296876         NA
## Q108342.fctr    -0.0211946324               0 0.0211946324         NA
## Q104996.fctr    -0.0218776356               0 0.0218776356         NA
## Q119650.fctr    -0.0222628005               0 0.0222628005         NA
## Q100689.fctr    -0.0263249102               0 0.0263249102         NA
## Q108617.fctr    -0.0285334447               0 0.0285334447         NA
## Q115195.fctr    -0.0295831061               0 0.0295831061         NA
## Q99716.fctr     -0.0333178411               0 0.0333178411         NA
## Q101163.fctr    -0.0349739760               0 0.0349739760         NA
## Q98059.fctr     -0.0354482758               0 0.0354482758         NA
## Q114961.fctr    -0.0396043459               0 0.0396043459         NA
## Hhold.fctr      -0.0644984804               0 0.0644984804         NA
## Q109244.fctr               NA               0           NA         NA
##                 freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## Q115611.fctr     1.346501    0.12200081   FALSE FALSE            FALSE
## Q113181.fctr     1.207806    0.12200081   FALSE FALSE            FALSE
## .clusterid       1.370319    0.16266775   FALSE FALSE            FALSE
## .clusterid.fctr  1.370319    0.16266775   FALSE FALSE            FALSE
## Q98197.fctr      1.293258    0.12200081   FALSE FALSE            FALSE
## Q116881.fctr     2.244592    0.12200081   FALSE FALSE            FALSE
## Q108855.fctr     1.511876    0.12200081   FALSE FALSE            FALSE
## Q106272.fctr     2.712785    0.12200081   FALSE FALSE            FALSE
## Q122771.fctr     3.656388    0.12200081   FALSE FALSE            FALSE
## Q123621.fctr     1.144186    0.12200081   FALSE FALSE            FALSE
## Q106388.fctr     2.478261    0.12200081   FALSE FALSE            FALSE
## Q110740.fctr     1.491409    0.12200081   FALSE FALSE            FALSE
## USER_ID          1.000000  100.00000000   FALSE FALSE            FALSE
## .pos             1.000000  100.00000000   FALSE FALSE            FALSE
## Q122769.fctr     1.693260    0.12200081   FALSE FALSE            FALSE
## Q120472.fctr     2.803119    0.12200081   FALSE FALSE            FALSE
## Q101596.fctr     1.737877    0.12200081   FALSE FALSE            FALSE
## Q119334.fctr     1.125395    0.12200081   FALSE FALSE            FALSE
## Q114152.fctr     2.270531    0.12200081   FALSE FALSE            FALSE
## Q98869.fctr      3.614191    0.12200081   FALSE FALSE            FALSE
## Q115899.fctr     1.448234    0.12200081   FALSE FALSE            FALSE
## Q116797.fctr     2.093023    0.12200081   FALSE FALSE            FALSE
## YOB.Age.dff      1.021687    0.73200488   FALSE FALSE            FALSE
## Q118232.fctr     1.307600    0.12200081   FALSE FALSE            FALSE
## Gender.fctr      2.405063    0.12200081   FALSE FALSE            FALSE
## Q105655.fctr     1.274947    0.12200081   FALSE FALSE            FALSE
## Q99480.fctr      4.095588    0.12200081   FALSE FALSE            FALSE
## Q123464.fctr     3.190731    0.12200081   FALSE FALSE            FALSE
## Q120650.fctr     3.544379    0.12200081   FALSE FALSE            FALSE
## Q122120.fctr     3.098563    0.12200081   FALSE FALSE            FALSE
## Q107869.fctr     1.293617    0.12200081   FALSE FALSE            FALSE
## Q120014.fctr     1.623324    0.12200081   FALSE FALSE            FALSE
## Q102289.fctr     2.286846    0.12200081   FALSE FALSE            FALSE
## Income.fctr      1.026596    0.28466856   FALSE FALSE            FALSE
## Q122770.fctr     1.407720    0.12200081   FALSE FALSE            FALSE
## Q111580.fctr     1.934936    0.12200081   FALSE FALSE            FALSE
## Q116601.fctr     3.948235    0.12200081   FALSE FALSE            FALSE
## Q117186.fctr     1.761702    0.12200081   FALSE FALSE            FALSE
## Q106993.fctr     4.903581    0.12200081   FALSE FALSE            FALSE
## Q112270.fctr     1.134313    0.12200081   FALSE FALSE            FALSE
## Q101162.fctr     1.556382    0.12200081   FALSE FALSE            FALSE
## Q108856.fctr     2.251156    0.12200081   FALSE FALSE            FALSE
## Q117193.fctr     1.346618    0.12200081   FALSE FALSE            FALSE
## Q116441.fctr     1.638601    0.12200081   FALSE FALSE            FALSE
## Q119851.fctr     1.575758    0.12200081   FALSE FALSE            FALSE
## Q111848.fctr     1.402247    0.12200081   FALSE FALSE            FALSE
## Q98578.fctr      1.717158    0.12200081   FALSE FALSE            FALSE
## Q118892.fctr     1.411356    0.12200081   FALSE FALSE            FALSE
## Q114386.fctr     1.426366    0.12200081   FALSE FALSE            FALSE
## Q120978.fctr     1.264000    0.12200081   FALSE FALSE            FALSE
## Q112512.fctr     4.175743    0.12200081   FALSE FALSE            FALSE
## Q102674.fctr     1.849030    0.12200081   FALSE FALSE            FALSE
## Q96024.fctr      1.683444    0.12200081   FALSE FALSE             TRUE
## Q108950.fctr     2.087366    0.12200081   FALSE FALSE             TRUE
## Q115610.fctr     3.966825    0.12200081   FALSE FALSE             TRUE
## YOB.Age.fctr     1.180000    0.36600244   FALSE FALSE             TRUE
## Q112478.fctr     1.459880    0.12200081   FALSE FALSE             TRUE
## Q116197.fctr     2.031484    0.12200081   FALSE FALSE             TRUE
## Q124742.fctr     1.322468    0.12200081   FALSE FALSE             TRUE
## Q106389.fctr     1.086957    0.12200081   FALSE FALSE             TRUE
## Edn.fctr         1.694524    0.32533550   FALSE FALSE             TRUE
## Q118117.fctr     1.402074    0.12200081   FALSE FALSE             TRUE
## Q100562.fctr     4.329082    0.12200081   FALSE FALSE             TRUE
## Q107491.fctr     6.288591    0.12200081   FALSE FALSE             TRUE
## Q116448.fctr     1.352326    0.12200081   FALSE FALSE             TRUE
## Q108754.fctr     2.045897    0.12200081   FALSE FALSE             TRUE
## Q116953.fctr     1.978788    0.12200081   FALSE FALSE             TRUE
## Q115602.fctr     3.864608    0.12200081   FALSE FALSE             TRUE
## Q118233.fctr     2.654676    0.12200081   FALSE FALSE             TRUE
## Q120012.fctr     1.344262    0.12200081   FALSE FALSE             TRUE
## Q118237.fctr     1.429419    0.12200081   FALSE FALSE             TRUE
## Q99581.fctr      4.989011    0.12200081   FALSE FALSE             TRUE
## .rnorm           1.000000  100.00000000   FALSE FALSE            FALSE
## Q120194.fctr     1.317536    0.12200081   FALSE FALSE            FALSE
## Q115777.fctr     1.387173    0.12200081   FALSE FALSE            FALSE
## Q106997.fctr     1.187117    0.12200081   FALSE FALSE            FALSE
## Q100680.fctr     1.250542    0.12200081   FALSE FALSE            FALSE
## Q113584.fctr     1.008824    0.12200081   FALSE FALSE            FALSE
## Q108343.fctr     1.565774    0.12200081   FALSE FALSE            FALSE
## Q121700.fctr     3.968468    0.12200081   FALSE FALSE            FALSE
## Q105840.fctr     1.486260    0.12200081   FALSE FALSE            FALSE
## Q120379.fctr     1.447596    0.12200081   FALSE FALSE            FALSE
## Q103293.fctr     1.252910    0.12200081   FALSE FALSE            FALSE
## Q124122.fctr     1.187500    0.12200081   FALSE FALSE            FALSE
## Q109367.fctr     1.460805    0.12200081   FALSE FALSE            FALSE
## Q113992.fctr     2.253086    0.12200081   FALSE FALSE            FALSE
## Q121699.fctr     2.600355    0.12200081   FALSE FALSE            FALSE
## Q121011.fctr     1.158969    0.12200081   FALSE FALSE            FALSE
## Q114748.fctr     1.344482    0.12200081   FALSE FALSE            FALSE
## Q106042.fctr     1.319690    0.12200081   FALSE FALSE            FALSE
## Q111220.fctr     3.014898    0.12200081   FALSE FALSE            FALSE
## Q114517.fctr     2.182796    0.12200081   FALSE FALSE            FALSE
## YOB              1.105769    2.92801952   FALSE FALSE            FALSE
## Q102687.fctr     1.004803    0.12200081   FALSE FALSE            FALSE
## Q102906.fctr     1.950213    0.12200081   FALSE FALSE            FALSE
## Q98078.fctr      1.590206    0.12200081   FALSE FALSE            FALSE
## Q115390.fctr     1.460576    0.12200081   FALSE FALSE            FALSE
## Q102089.fctr     2.381260    0.12200081   FALSE FALSE            FALSE
## Q100010.fctr     3.962529    0.12200081   FALSE FALSE            FALSE
## Q99982.fctr      1.107143    0.12200081   FALSE FALSE            FALSE
## Q113583.fctr     1.881690    0.12200081   FALSE FALSE            FALSE
## Q108342.fctr     2.426563    0.12200081   FALSE FALSE            FALSE
## Q104996.fctr     1.032350    0.12200081   FALSE FALSE            FALSE
## Q119650.fctr     3.108830    0.12200081   FALSE FALSE            FALSE
## Q100689.fctr     1.440137    0.12200081   FALSE FALSE            FALSE
## Q108617.fctr     7.888446    0.12200081   FALSE FALSE            FALSE
## Q115195.fctr     1.713147    0.12200081   FALSE FALSE            FALSE
## Q99716.fctr      4.852332    0.12200081   FALSE FALSE            FALSE
## Q101163.fctr     1.437576    0.12200081   FALSE FALSE            FALSE
## Q98059.fctr      5.379888    0.12200081   FALSE FALSE            FALSE
## Q114961.fctr     1.029000    0.12200081   FALSE FALSE            FALSE
## Hhold.fctr       1.209330    0.28466856   FALSE FALSE            FALSE
## Q109244.fctr     0.000000    0.04066694    TRUE  TRUE               NA
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

##              cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## Q109244.fctr    NA               0        NA         NA         0
##              percentUnique zeroVar  nzv is.cor.y.abs.low
## Q109244.fctr    0.04066694    TRUE TRUE               NA
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "numeric data missing in : "
##        YOB Party.fctr 
##        128        622 
## [1] "numeric data w/ 0s in : "
## YOB.Age.dff 
##         136 
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
##          Gender          Income HouseholdStatus  EducationLevel 
##              46             445             177             410 
##           Party         Q124742         Q124122         Q123464 
##              NA            1438             823             708 
##         Q123621         Q122769         Q122770         Q122771 
##             778             644             594             587 
##         Q122120         Q121699         Q121700         Q120978 
##             585             547             563             599 
##         Q121011         Q120379         Q120650         Q120472 
##             571             607             655             649 
##         Q120194         Q120012         Q120014         Q119334 
##             654             591             641             568 
##         Q119851         Q119650         Q118892         Q118117 
##             540             578             486             479 
##         Q118232         Q118233         Q118237         Q117186 
##             701             554             539             648 
##         Q117193         Q116797         Q116881         Q116953 
##             655             590             635             616 
##         Q116601         Q116441         Q116448         Q116197 
##             534             541             560             551 
##         Q115602         Q115777         Q115610         Q115611 
##             539             578             537             487 
##         Q115899         Q115390         Q114961         Q114748 
##             573             619             538             447 
##         Q115195         Q114517         Q114386         Q113992 
##             525             481             521             447 
##         Q114152         Q113583         Q113584         Q113181 
##             537             514             512             453 
##         Q112478         Q112512         Q112270         Q111848 
##             494             460             521             398 
##         Q111580         Q111220         Q110740         Q109367 
##             474             379             357             168 
##         Q108950         Q109244         Q108855         Q108617 
##             204               0             438             288 
##         Q108856         Q108754         Q108342         Q108343 
##             436             338             341             333 
##         Q107869         Q107491         Q106993         Q106997 
##             389             366             389             396 
##         Q106272         Q106388         Q106389         Q106042 
##             426             476             495             451 
##         Q105840         Q105655         Q104996         Q103293 
##             487             393             400             431 
##         Q102906         Q102674         Q102687         Q102289 
##             493             511             475             484 
##         Q102089         Q101162         Q101163         Q101596 
##             462             498             572             477 
##         Q100689         Q100680         Q100562          Q99982 
##             414             497             487             514 
##         Q100010          Q99716          Q99581          Q99480 
##             445             500             466             478 
##          Q98869          Q98578          Q98059          Q98078 
##             564             542             450             569 
##          Q98197          Q96024            .lcn 
##             528             550             622
## [1] "glb_feats_df:"
## [1] 113  12
##                    id exclude.as.feat rsp_var
## Party.fctr Party.fctr            TRUE    TRUE
##                    id      cor.y exclude.as.feat  cor.y.abs cor.high.X
## USER_ID       USER_ID 0.04512535            TRUE 0.04512535         NA
## Party.fctr Party.fctr         NA            TRUE         NA         NA
##            freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## USER_ID            1           100   FALSE FALSE            FALSE
## Party.fctr        NA            NA      NA    NA               NA
##            interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID                <NA>                   NA       FALSE   TRUE
## Party.fctr             <NA>                   NA          NA     NA
##            rsp_var
## USER_ID         NA
## Party.fctr    TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
##              label step_major step_minor label_minor     bgn     end
## 13 select.features          6          0           0 110.583 113.283
## 14      fit.models          7          0           0 113.283      NA
##    elapsed
## 13     2.7
## 14      NA

Step 7.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 113.809  NA      NA
# load(paste0(glbOut$pfx, "dsk.RData"))

glbgetModelSelectFormula <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

glbgetDisplayModelsDf <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(glbgetModelSelectFormula(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(glbgetModelSelectFormula(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#glbgetDisplayModelsDf()

glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}
##              label step_major step_minor   label_minor     bgn     end
## 1 fit.models_0_bgn          1          0         setup 113.809 113.845
## 2 fit.models_0_MFO          1          1 myMFO_classfr 113.845      NA
##   elapsed
## 1   0.036
## 2      NA
## [1] "myfit_mdl: enter: 0.002000 secs"
## [1] "myfit_mdl: fitting model: MFO###myMFO_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.410000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] D R
## Levels: D R
## [1] "unique.prob:"
## y
##         R         D 
## 0.5789474 0.4210526 
## [1] "MFO.val:"
## [1] "R"
## [1] "myfit_mdl: train complete: 0.891000 secs"
##   parameter
## 1      none
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used
## [1] "myfit_mdl: train diagnostics complete: 0.894000 secs"
## Loading required namespace: pROC
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
## [1] "in MFO.Classifier$prob"
##           D         R
## 1 0.5789474 0.4210526
## 2 0.5789474 0.4210526
## 3 0.5789474 0.4210526
## 4 0.5789474 0.4210526
## 5 0.5789474 0.4210526
## 6 0.5789474 0.4210526

##          Prediction
## Reference    D    R
##         D    0  824
##         R    0 1133
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.789474e-01   0.000000e+00   5.567125e-01   6.009453e-01   5.789474e-01 
## AccuracyPValue  McnemarPValue 
##   5.096118e-01  8.885492e-181 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##           D         R
## 1 0.5789474 0.4210526
## 2 0.5789474 0.4210526
## 3 0.5789474 0.4210526
## 4 0.5789474 0.4210526
## 5 0.5789474 0.4210526
## 6 0.5789474 0.4210526
##          Prediction
## Reference   D   R
##         D   0 214
##         R   0 288
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.737052e-01   0.000000e+00   5.291204e-01   6.174170e-01   5.737052e-01 
## AccuracyPValue  McnemarPValue 
##   5.188758e-01   5.017792e-48 
## [1] "myfit_mdl: predict complete: 6.695000 secs"
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                      0.473
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.003             0.5            0            1
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.4       0.7333333        0.5789474
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5567125             0.6009453             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            0            1             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.4       0.7291139        0.5737052
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5291204              0.617417             0
## [1] "in MFO.Classifier$prob"
##           D         R
## 1 0.5789474 0.4210526
## 2 0.5789474 0.4210526
## 3 0.5789474 0.4210526
## 4 0.5789474 0.4210526
## 5 0.5789474 0.4210526
## 6 0.5789474 0.4210526
## [1] "myfit_mdl: exit: 6.767000 secs"
##                 label step_major step_minor      label_minor     bgn
## 2    fit.models_0_MFO          1          1    myMFO_classfr 113.845
## 3 fit.models_0_Random          1          2 myrandom_classfr 120.619
##       end elapsed
## 2 120.618   6.773
## 3      NA      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Random###myrandom_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.410000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.681000 secs"
##   parameter
## 1      none
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## Warning in if (mdl_specs_lst[["train.method"]] == "glm")
## mydisplayOutliers(mdl, : the condition has length > 1 and only the first
## element will be used

## [1] "myfit_mdl: train diagnostics complete: 0.683000 secs"
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference    D    R
##         D    0  824
##         R    0 1133
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.789474e-01   0.000000e+00   5.567125e-01   6.009453e-01   5.789474e-01 
## AccuracyPValue  McnemarPValue 
##   5.096118e-01  8.885492e-181 
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   D   R
##         D   0 214
##         R   0 288
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.737052e-01   0.000000e+00   5.291204e-01   6.174170e-01   5.737052e-01 
## AccuracyPValue  McnemarPValue 
##   5.188758e-01   5.017792e-48 
## [1] "myfit_mdl: predict complete: 6.812000 secs"
##                          id  feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.266                 0.002       0.4992277
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.4150485    0.5834069       0.5146734                    0.4
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.7333333        0.5789474             0.5567125
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.6009453             0       0.4990752    0.4252336
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.5729167       0.5181075                    0.4       0.7291139
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5737052             0.5291204              0.617417
##   max.Kappa.OOB
## 1             0
## [1] "in Random.Classifier$prob"
## [1] "myfit_mdl: exit: 7.614000 secs"
# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")
##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##       bgn     end elapsed
## 3 120.619 128.245   7.627
## 4 128.246      NA      NA
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr"
## [1] "myfit_mdl: setup complete: 0.683000 secs"
## Loading required package: glmnet
## Loading required package: Matrix
## 
## Attaching package: 'Matrix'
## The following object is masked from 'package:tidyr':
## 
##     expand
## Loaded glmnet 2.0-5
## Fitting alpha = 0.1, lambda = 0.00166 on full training set
## [1] "myfit_mdl: train complete: 1.411000 secs"
##   alpha      lambda
## 1   0.1 0.001656204

##             Length Class      Mode     
## a0           48    -none-     numeric  
## beta        192    dgCMatrix  S4       
## df           48    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       48    -none-     numeric  
## dev.ratio    48    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        4    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##     (Intercept)  Q113181.fctrNo Q113181.fctrYes  Q115611.fctrNo 
##       0.3053858      -0.2963546       0.4135322      -0.2362299 
## Q115611.fctrYes 
##       0.3448103 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"     "Q113181.fctrNo"  "Q113181.fctrYes" "Q115611.fctrNo" 
## [5] "Q115611.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 1.518000 secs"

##          Prediction
## Reference   D   R
##         D 379 445
##         R 306 827
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.162494e-01   1.943425e-01   5.942872e-01   6.378625e-01   5.789474e-01 
## AccuracyPValue  McnemarPValue 
##   4.295434e-04   4.761268e-07

##          Prediction
## Reference   D   R
##         D   0 214
##         R   0 288
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.737052e-01   0.000000e+00   5.291204e-01   6.174170e-01   5.737052e-01 
## AccuracyPValue  McnemarPValue 
##   5.188758e-01   5.017792e-48 
## [1] "myfit_mdl: predict complete: 7.108000 secs"
##                           id                     feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q115611.fctr,Q113181.fctr               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      0.721                 0.028       0.5771183
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.3519417    0.8022948       0.6256871                   0.55
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6877339        0.6162494             0.5942872
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.6378625     0.1943425       0.5338785     0.317757
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1         0.75       0.5592306                    0.4       0.7291139
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5737052             0.5291204              0.617417
##   max.Kappa.OOB
## 1             0
## [1] "myfit_mdl: exit: 7.177000 secs"
if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr"
## [1] "myfit_mdl: setup complete: 0.674000 secs"
## Loading required package: rpart
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0105 on full training set
## [1] "myfit_mdl: train complete: 2.096000 secs"
## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 1957 
## 
##           CP nsplit rel error
## 1 0.04186893      0 1.0000000
## 2 0.01046723      2 0.9162621
## 
## Variable importance
## Q113181.fctrYes  Q113181.fctrNo  Q115611.fctrNo Q115611.fctrYes 
##              41              29              19              11 
## 
## Node number 1: 1957 observations,    complexity param=0.04186893
##   predicted class=R  expected loss=0.4210526  P(node) =1
##     class counts:   824  1133
##    probabilities: 0.421 0.579 
##   left son=2 (1207 obs) right son=3 (750 obs)
##   Primary splits:
##       Q113181.fctrYes < 0.5 to the left,  improve=26.84038, (0 missing)
##       Q113181.fctrNo  < 0.5 to the right, improve=23.63967, (0 missing)
##       Q115611.fctrYes < 0.5 to the left,  improve=19.10792, (0 missing)
##       Q115611.fctrNo  < 0.5 to the right, improve=16.37764, (0 missing)
##   Surrogate splits:
##       Q113181.fctrNo < 0.5 to the right, agree=0.852, adj=0.613, (0 split)
## 
## Node number 2: 1207 observations,    complexity param=0.04186893
##   predicted class=R  expected loss=0.4863297  P(node) =0.6167603
##     class counts:   587   620
##    probabilities: 0.486 0.514 
##   left son=4 (599 obs) right son=5 (608 obs)
##   Primary splits:
##       Q115611.fctrNo  < 0.5 to the right, improve=12.07893, (0 missing)
##       Q115611.fctrYes < 0.5 to the left,  improve=10.61765, (0 missing)
##       Q113181.fctrNo  < 0.5 to the right, improve= 2.33422, (0 missing)
##   Surrogate splits:
##       Q115611.fctrYes < 0.5 to the left,  agree=0.794, adj=0.584, (0 split)
##       Q113181.fctrNo  < 0.5 to the right, agree=0.596, adj=0.185, (0 split)
## 
## Node number 3: 750 observations
##   predicted class=R  expected loss=0.316  P(node) =0.3832397
##     class counts:   237   513
##    probabilities: 0.316 0.684 
## 
## Node number 4: 599 observations
##   predicted class=D  expected loss=0.442404  P(node) =0.3060807
##     class counts:   334   265
##    probabilities: 0.558 0.442 
## 
## Node number 5: 608 observations
##   predicted class=R  expected loss=0.4161184  P(node) =0.3106796
##     class counts:   253   355
##    probabilities: 0.416 0.584 
## 
## n= 1957 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 1957 824 R (0.4210526 0.5789474)  
##   2) Q113181.fctrYes< 0.5 1207 587 R (0.4863297 0.5136703)  
##     4) Q115611.fctrNo>=0.5 599 265 D (0.5575960 0.4424040) *
##     5) Q115611.fctrNo< 0.5 608 253 R (0.4161184 0.5838816) *
##   3) Q113181.fctrYes>=0.5 750 237 R (0.3160000 0.6840000) *
## [1] "myfit_mdl: train diagnostics complete: 2.910000 secs"

## [1] "mypredict_mdl: maxMetricDf:"
##    threshold   f.score  accuracy
## 10      0.45 0.6969089 0.6142054
## 11      0.50 0.6969089 0.6142054
## 12      0.55 0.6969089 0.6142054

##          Prediction
## Reference   D   R
##         D 334 490
##         R 265 868
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.142054e-01   1.780783e-01   5.922249e-01   6.358431e-01   5.789474e-01 
## AccuracyPValue  McnemarPValue 
##   8.214160e-04   3.573784e-16

##          Prediction
## Reference   D   R
##         D   0 214
##         R   0 288
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.737052e-01   0.000000e+00   5.291204e-01   6.174170e-01   5.737052e-01 
## AccuracyPValue  McnemarPValue 
##   5.188758e-01   5.017792e-48 
## [1] "myfit_mdl: predict complete: 8.849000 secs"
##                     id                     feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q115611.fctr,Q113181.fctr               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.415                 0.015       0.5857237
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.4053398    0.7661077       0.6101745                    0.5
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6969089        0.6104535             0.5922249
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.6358431      0.177199       0.5334729    0.3551402
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.7118056       0.5325318                    0.4       0.7291139
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5737052             0.5291204              0.617417
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1             0         0.01616114      0.02629543
## [1] "myfit_mdl: exit: 8.911000 secs"
if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
##                            label step_major step_minor label_minor     bgn
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet 128.246
## 5         fit.models_0_Low.cor.X          1          4      glmnet 144.381
##      end elapsed
## 4 144.38  16.134
## 5     NA      NA
indepVar <- mygetIndepVar(glb_feats_df)
indepVar <- setdiff(indepVar, unique(glb_feats_df$cor.high.X))
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.692000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0357 on full training set
## [1] "myfit_mdl: train complete: 16.276000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             84  -none-     numeric  
## beta        21840  dgCMatrix  S4       
## df             84  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         84  -none-     numeric  
## dev.ratio      84  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        260  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##           (Intercept)         Hhold.fctrMKy         Hhold.fctrPKn 
##           0.221440531           0.088740320          -0.363049495 
##         Hhold.fctrPKy         Income.fctr.Q         Income.fctr.C 
##          -0.066620973           0.023323191           0.013246595 
##        Q100562.fctrNo       Q101163.fctrDad       Q106272.fctrYes 
##          -0.037681183           0.100162253           0.076786887 
##        Q106997.fctrGr        Q110740.fctrPC        Q113181.fctrNo 
##           0.060299822           0.043782927          -0.176988082 
##       Q113181.fctrYes        Q115611.fctrNo       Q115611.fctrYes 
##           0.230691777          -0.159469858           0.181404742 
##        Q115899.fctrCs     Q116881.fctrHappy     Q116881.fctrRight 
##          -0.021861438          -0.006195520           0.087679839 
##    Q119650.fctrGiving Q119650.fctrReceiving         Q98197.fctrNo 
##           0.015633046          -0.005378502          -0.138631965 
##         Q98869.fctrNo         Q99480.fctrNo 
##          -0.012566215          -0.016768438 
## [1] "max lambda < lambdaOpt:"
##                    (Intercept)                  Hhold.fctrMKy 
##                    0.216762556                    0.094569421 
##                  Hhold.fctrPKn                  Hhold.fctrPKy 
##                   -0.403641466                   -0.147928827 
##                  Hhold.fctrSKy                  Income.fctr.Q 
##                   -0.028492249                    0.046046298 
##                  Income.fctr.C                 Q100562.fctrNo 
##                    0.037396969                   -0.058607469 
##                Q101163.fctrDad                Q106272.fctrYes 
##                    0.113053847                    0.086518373 
##                 Q106997.fctrGr                 Q106997.fctrYy 
##                    0.074861094                   -0.007176625 
##                 Q110740.fctrPC                 Q113181.fctrNo 
##                    0.057142311                   -0.182381789 
##                Q113181.fctrYes                 Q115611.fctrNo 
##                    0.232846900                   -0.165108486 
##                Q115611.fctrYes                 Q115899.fctrCs 
##                    0.187103110                   -0.036662653 
##              Q116881.fctrHappy              Q116881.fctrRight 
##                   -0.017190537                    0.093784522 
##             Q119650.fctrGiving          Q119650.fctrReceiving 
##                    0.026231177                   -0.011093638 
##                  Q98197.fctrNo                  Q98869.fctrNo 
##                   -0.145180735                   -0.027405096 
##                  Q99480.fctrNo Hhold.fctrMKy:.clusterid.fctr4 
##                   -0.027633363                    0.009349452 
## [1] "myfit_mdl: train diagnostics complete: 16.956000 secs"

##          Prediction
## Reference   D   R
##         D 266 558
##         R 161 972
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.326009e-01   1.934227e-01   6.108001e-01   6.540036e-01   5.789474e-01 
## AccuracyPValue  McnemarPValue 
##   7.327469e-07   2.345270e-49

## [1] "mypredict_mdl: maxMetricDf:"
##    threshold   f.score  accuracy
## 1       0.00 0.7291139 0.5737052
## 2       0.05 0.7291139 0.5737052
## 3       0.10 0.7291139 0.5737052
## 4       0.15 0.7291139 0.5737052
## 5       0.20 0.7291139 0.5737052
## 6       0.25 0.7291139 0.5737052
## 7       0.30 0.7291139 0.5737052
## 8       0.35 0.7291139 0.5737052
## 9       0.40 0.7291139 0.5737052
## 10      0.45 0.7213542 0.5737052
## 12      0.55 0.6409396 0.5737052

##          Prediction
## Reference   D   R
##         D  11 203
##         R  11 277
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.737052e-01   1.492811e-02   5.291204e-01   6.174170e-01   5.737052e-01 
## AccuracyPValue  McnemarPValue 
##   5.188758e-01   5.835606e-39 
## [1] "myfit_mdl: predict complete: 26.440000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     15.497                 1.772
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5903575    0.3228155    0.8578994        0.661983
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.5       0.7300038        0.6050093
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6108001             0.6540036      0.128624
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5091186    0.2196262    0.7986111       0.5816378
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.45       0.7213542        0.5737052
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5291204              0.617417    0.01492811
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01148221      0.02835557
## [1] "myfit_mdl: exit: 26.768000 secs"
fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")
##                    label step_major step_minor label_minor     bgn     end
## 5 fit.models_0_Low.cor.X          1          4      glmnet 144.381 171.189
## 6       fit.models_0_end          1          5    teardown 171.190      NA
##   elapsed
## 5  26.809
## 6      NA
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##         label step_major step_minor label_minor     bgn     end elapsed
## 14 fit.models          7          0           0 113.283 171.204  57.922
## 15 fit.models          7          1           1 171.205      NA      NA

```{r fit.models_1, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}

##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 174.976  NA      NA
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 174.976 174.988
## 2 fit.models_1_All.X          1          1       setup 174.989      NA
##   elapsed
## 1   0.012
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 174.989 174.997
## 3 fit.models_1_All.X          1          2      glmnet 174.997      NA
##   elapsed
## 2   0.008
## 3      NA
## [1] "myfit_mdl: enter: 0.001000 secs"
## [1] "myfit_mdl: fitting model: All.X##rcv#glmnet"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.709000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0637 on full training set
## [1] "myfit_mdl: train complete: 16.547000 secs"

##             Length Class      Mode     
## a0             86  -none-     numeric  
## beta        22360  dgCMatrix  S4       
## df             86  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         86  -none-     numeric  
## dev.ratio      86  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        260  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##        (Intercept)      Hhold.fctrMKy      Hhold.fctrPKn 
##       0.2406428928       0.0762870273      -0.2953396040 
##     Q100562.fctrNo    Q101163.fctrDad    Q106272.fctrYes 
##      -0.0110508029       0.0800696434       0.0664514859 
##     Q106997.fctrGr     Q110740.fctrPC     Q113181.fctrNo 
##       0.0347221725       0.0274085076      -0.1636826446 
##    Q113181.fctrYes     Q115611.fctrNo    Q115611.fctrYes 
##       0.2111681078      -0.1409573482       0.1699102327 
##     Q115899.fctrCs  Q116881.fctrHappy  Q116881.fctrRight 
##      -0.0047139261      -0.0001640877       0.0711299222 
## Q119650.fctrGiving      Q98197.fctrNo      Q98869.fctrNo 
##       0.0099743987      -0.1280674998      -0.0064004001 
##      Q99480.fctrNo 
##      -0.0105142415 
## [1] "max lambda < lambdaOpt:"
##           (Intercept)         Hhold.fctrMKy         Hhold.fctrPKn 
##           0.231137831           0.084903152          -0.334415212 
##         Hhold.fctrPKy         Income.fctr.Q         Income.fctr.C 
##          -0.065222397           0.020496301           0.010335205 
##        Q100562.fctrNo       Q101163.fctrDad       Q106272.fctrYes 
##          -0.032098432           0.093074498           0.075016818 
##        Q106997.fctrGr        Q110740.fctrPC        Q113181.fctrNo 
##           0.053588195           0.040691709          -0.170206282 
##       Q113181.fctrYes        Q115611.fctrNo       Q115611.fctrYes 
##           0.215390053          -0.148615954           0.174916163 
##        Q115899.fctrCs     Q116881.fctrHappy     Q116881.fctrRight 
##          -0.019155211          -0.011048021           0.078702675 
##    Q119650.fctrGiving Q119650.fctrReceiving         Q98197.fctrNo 
##           0.019371664          -0.005670503          -0.135056904 
##         Q98869.fctrNo         Q99480.fctrNo 
##          -0.019568656          -0.021088646 
## [1] "myfit_mdl: train diagnostics complete: 17.191000 secs"

##          Prediction
## Reference   D   R
##         D 247 577
##         R 143 990
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.320899e-01   1.869725e-01   6.102837e-01   6.534996e-01   5.789474e-01 
## AccuracyPValue  McnemarPValue 
##   9.237623e-07   1.402889e-58

##          Prediction
## Reference   D   R
##         D  97 117
##         R  94 194
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.5796813      0.1286586      0.5351418      0.6232769      0.5737052 
## AccuracyPValue  McnemarPValue 
##      0.4115799      0.1298883 
## [1] "myfit_mdl: predict complete: 26.950000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      15.75                 1.798
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5867718    0.2997573    0.8737864       0.6590272
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.5       0.7333333        0.6055195
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6102837             0.6534996     0.1238626
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5067173    0.2009346       0.8125       0.5801856
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.55       0.6477462        0.5796813
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5351418             0.6232769     0.1286586
##   max.AccuracySD.fit max.KappaSD.fit
## 1          0.0117432      0.02938213
## [1] "myfit_mdl: exit: 27.232000 secs"
##                  label step_major step_minor label_minor     bgn     end
## 3   fit.models_1_All.X          1          2      glmnet 174.997 202.254
## 4 fit.models_1_preProc          1          3     preProc 202.254      NA
##   elapsed
## 3  27.257
## 4      NA
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
##                                                    id
## All.X##rcv#glmnet                   All.X##rcv#glmnet
## Low.cor.X##rcv#glmnet           Low.cor.X##rcv#glmnet
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart             Max.cor.Y##rcv#rpart
## Random###myrandom_classfr   Random###myrandom_classfr
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## All.X##rcv#glmnet          Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## Low.cor.X##rcv#glmnet      Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Q115611.fctr,Q113181.fctr
## Max.cor.Y##rcv#rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Q115611.fctr,Q113181.fctr
## Random###myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         .rnorm
##                            max.nTuningRuns min.elapsedtime.everything
## All.X##rcv#glmnet                       25                     15.750
## Low.cor.X##rcv#glmnet                   25                     15.497
## Max.cor.Y.rcv.1X1###glmnet               0                      0.721
## Max.cor.Y##rcv#rpart                     5                      1.415
## Random###myrandom_classfr                0                      0.266
##                            min.elapsedtime.final max.AUCpROC.fit
## All.X##rcv#glmnet                          1.798       0.5867718
## Low.cor.X##rcv#glmnet                      1.772       0.5903575
## Max.cor.Y.rcv.1X1###glmnet                 0.028       0.5771183
## Max.cor.Y##rcv#rpart                       0.015       0.5857237
## Random###myrandom_classfr                  0.002       0.4992277
##                            max.Sens.fit max.Spec.fit max.AUCROCR.fit
## All.X##rcv#glmnet             0.2997573    0.8737864       0.6590272
## Low.cor.X##rcv#glmnet         0.3228155    0.8578994       0.6619830
## Max.cor.Y.rcv.1X1###glmnet    0.3519417    0.8022948       0.6256871
## Max.cor.Y##rcv#rpart          0.4053398    0.7661077       0.6101745
## Random###myrandom_classfr     0.4150485    0.5834069       0.5146734
##                            opt.prob.threshold.fit max.f.score.fit
## All.X##rcv#glmnet                            0.50       0.7333333
## Low.cor.X##rcv#glmnet                        0.50       0.7300038
## Max.cor.Y.rcv.1X1###glmnet                   0.55       0.6877339
## Max.cor.Y##rcv#rpart                         0.50       0.6969089
## Random###myrandom_classfr                    0.40       0.7333333
##                            max.Accuracy.fit max.AccuracyLower.fit
## All.X##rcv#glmnet                 0.6055195             0.6102837
## Low.cor.X##rcv#glmnet             0.6050093             0.6108001
## Max.cor.Y.rcv.1X1###glmnet        0.6162494             0.5942872
## Max.cor.Y##rcv#rpart              0.6104535             0.5922249
## Random###myrandom_classfr         0.5789474             0.5567125
##                            max.AccuracyUpper.fit max.Kappa.fit
## All.X##rcv#glmnet                      0.6534996     0.1238626
## Low.cor.X##rcv#glmnet                  0.6540036     0.1286240
## Max.cor.Y.rcv.1X1###glmnet             0.6378625     0.1943425
## Max.cor.Y##rcv#rpart                   0.6358431     0.1771990
## Random###myrandom_classfr              0.6009453     0.0000000
##                            max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## All.X##rcv#glmnet                0.5067173    0.2009346    0.8125000
## Low.cor.X##rcv#glmnet            0.5091186    0.2196262    0.7986111
## Max.cor.Y.rcv.1X1###glmnet       0.5338785    0.3177570    0.7500000
## Max.cor.Y##rcv#rpart             0.5334729    0.3551402    0.7118056
## Random###myrandom_classfr        0.4990752    0.4252336    0.5729167
##                            max.AUCROCR.OOB opt.prob.threshold.OOB
## All.X##rcv#glmnet                0.5801856                   0.55
## Low.cor.X##rcv#glmnet            0.5816378                   0.45
## Max.cor.Y.rcv.1X1###glmnet       0.5592306                   0.40
## Max.cor.Y##rcv#rpart             0.5325318                   0.40
## Random###myrandom_classfr        0.5181075                   0.40
##                            max.f.score.OOB max.Accuracy.OOB
## All.X##rcv#glmnet                0.6477462        0.5796813
## Low.cor.X##rcv#glmnet            0.7213542        0.5737052
## Max.cor.Y.rcv.1X1###glmnet       0.7291139        0.5737052
## Max.cor.Y##rcv#rpart             0.7291139        0.5737052
## Random###myrandom_classfr        0.7291139        0.5737052
##                            max.AccuracyLower.OOB max.AccuracyUpper.OOB
## All.X##rcv#glmnet                      0.5351418             0.6232769
## Low.cor.X##rcv#glmnet                  0.5291204             0.6174170
## Max.cor.Y.rcv.1X1###glmnet             0.5291204             0.6174170
## Max.cor.Y##rcv#rpart                   0.5291204             0.6174170
## Random###myrandom_classfr              0.5291204             0.6174170
##                            max.Kappa.OOB max.AccuracySD.fit
## All.X##rcv#glmnet             0.12865863         0.01174320
## Low.cor.X##rcv#glmnet         0.01492811         0.01148221
## Max.cor.Y.rcv.1X1###glmnet    0.00000000                 NA
## Max.cor.Y##rcv#rpart          0.00000000         0.01616114
## Random###myrandom_classfr     0.00000000                 NA
##                            max.KappaSD.fit
## All.X##rcv#glmnet               0.02938213
## Low.cor.X##rcv#glmnet           0.02835557
## Max.cor.Y.rcv.1X1###glmnet              NA
## Max.cor.Y##rcv#rpart            0.02629543
## Random###myrandom_classfr               NA
##                            min.elapsedtime.everything
## Random###myrandom_classfr                       0.266
## MFO###myMFO_classfr                             0.473
## Max.cor.Y.rcv.1X1###glmnet                      0.721
## Max.cor.Y##rcv#rpart                            1.415
## Low.cor.X##rcv#glmnet                          15.497
## All.X##rcv#glmnet                              15.750
##                  label step_major step_minor label_minor     bgn     end
## 4 fit.models_1_preProc          1          3     preProc 202.254 203.243
## 5     fit.models_1_end          1          4    teardown 203.244      NA
##   elapsed
## 4   0.989
## 5      NA
##         label step_major step_minor label_minor     bgn     end elapsed
## 15 fit.models          7          1           1 171.205 203.253  32.048
## 16 fit.models          7          2           2 203.254      NA      NA

```{r fit.models_2, cache=FALSE, fig.height=10, fig.width=15, eval=myevlChunk(glbChunks, glbOut$pfx)}

##              label step_major step_minor label_minor    bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 204.42  NA      NA
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
## Warning: Removed 3 rows containing missing values (geom_errorbar).
## quartz_off_screen 
##                 2
## Warning: Removed 3 rows containing missing values (geom_errorbar).

##                           id max.Accuracy.OOB max.AUCROCR.OOB
## 6          All.X##rcv#glmnet        0.5796813       0.5801856
## 5      Low.cor.X##rcv#glmnet        0.5737052       0.5816378
## 3 Max.cor.Y.rcv.1X1###glmnet        0.5737052       0.5592306
## 4       Max.cor.Y##rcv#rpart        0.5737052       0.5325318
## 2  Random###myrandom_classfr        0.5737052       0.5181075
## 1        MFO###myMFO_classfr        0.5737052       0.5000000
##   max.AUCpROC.OOB min.elapsedtime.everything max.Accuracy.fit
## 6       0.5067173                     15.750        0.6055195
## 5       0.5091186                     15.497        0.6050093
## 3       0.5338785                      0.721        0.6162494
## 4       0.5334729                      1.415        0.6104535
## 2       0.4990752                      0.266        0.5789474
## 1       0.5000000                      0.473        0.5789474
##   opt.prob.threshold.fit opt.prob.threshold.OOB
## 6                   0.50                   0.55
## 5                   0.50                   0.45
## 3                   0.55                   0.40
## 4                   0.50                   0.40
## 2                   0.40                   0.40
## 1                   0.40                   0.40
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB + min.elapsedtime.everything - 
##     max.Accuracy.fit - opt.prob.threshold.OOB
## <environment: 0x7fbe4e7a9f88>
## [1] "Best model id: All.X##rcv#glmnet"
## [1] "User specified selection: All.X##rcv#glmnet"
## glmnet 
## 
## 1957 samples
##  108 predictor
##    2 classes: 'D', 'R' 
## 
## No pre-processing
## Resampling: Cross-Validated (3 fold, repeated 3 times) 
## Summary of sample sizes: 1305, 1305, 1304, 1304, 1305, 1305, ... 
## Resampling results across tuning parameters:
## 
##   alpha  lambda      Accuracy   Kappa     
##   0.100  0.03568184  0.5845620  0.11772319
##   0.100  0.05000000  0.5894996  0.12266322
##   0.100  0.06367626  0.5929103  0.12598392
##   0.100  0.07000000  0.5961471  0.13104131
##   0.100  0.09167068  0.5978500  0.12744153
##   0.325  0.03568184  0.5995549  0.13220824
##   0.325  0.05000000  0.6046682  0.13180726
##   0.325  0.06367626  0.6055195  0.12386264
##   0.325  0.07000000  0.6017716  0.11086277
##   0.325  0.09167068  0.5934247  0.07302233
##   0.550  0.03568184  0.6050093  0.12862401
##   0.550  0.05000000  0.5990460  0.09952083
##   0.550  0.06367626  0.5872918  0.04483622
##   0.550  0.07000000  0.5816741  0.01389986
##   0.550  0.09167068  0.5789474  0.00000000
##   0.775  0.03568184  0.5990463  0.10159305
##   0.775  0.05000000  0.5815036  0.01367876
##   0.775  0.06367626  0.5789474  0.00000000
##   0.775  0.07000000  0.5789474  0.00000000
##   0.775  0.09167068  0.5789474  0.00000000
##   1.000  0.03568184  0.5901878  0.05538669
##   1.000  0.05000000  0.5789474  0.00000000
##   1.000  0.06367626  0.5789474  0.00000000
##   1.000  0.07000000  0.5789474  0.00000000
##   1.000  0.09167068  0.5789474  0.00000000
## 
## Accuracy was used to select the optimal model using  the largest value.
## The final values used for the model were alpha = 0.325 and lambda
##  = 0.06367626.
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
##                                 All.X..rcv.glmnet.imp        imp
## Hhold.fctrPKn                              100.000000 100.000000
## Q113181.fctrYes                             64.984274  64.984274
## Q115611.fctrYes                             52.729697  52.729697
## Q113181.fctrNo                              51.264404  51.264404
## Q115611.fctrNo                              44.707621  44.707621
## Q98197.fctrNo                               40.627886  40.627886
## Q101163.fctrDad                             27.773431  27.773431
## Hhold.fctrMKy                               25.424438  25.424438
## Q116881.fctrRight                           23.579086  23.579086
## Q106272.fctrYes                             22.437747  22.437747
## Hhold.fctrPKy                               17.918579  17.918579
## Q106997.fctrGr                              15.677653  15.677653
## Q110740.fctrPC                              11.933368  11.933368
## Q100562.fctrNo                               9.122468   9.122468
## Q99480.fctrNo                                6.082982   6.082982
## Income.fctr.Q                                5.630958   5.630958
## Q119650.fctrGiving                           5.596421   5.596421
## Q98869.fctrNo                                5.552206   5.552206
## Q115899.fctrCs                               5.392219   5.392219
## Q116881.fctrHappy                            3.039743   3.039743
## Income.fctr.C                                2.839396   2.839396
## Q119650.fctrReceiving                        1.557860   1.557860
## .rnorm                                       0.000000   0.000000
## Edn.fctr.L                                   0.000000   0.000000
## Edn.fctr.Q                                   0.000000   0.000000
## Edn.fctr.C                                   0.000000   0.000000
## Edn.fctr^4                                   0.000000   0.000000
## Edn.fctr^5                                   0.000000   0.000000
## Edn.fctr^6                                   0.000000   0.000000
## Edn.fctr^7                                   0.000000   0.000000
## Gender.fctrF                                 0.000000   0.000000
## Gender.fctrM                                 0.000000   0.000000
## Hhold.fctrMKn                                0.000000   0.000000
## Hhold.fctrSKn                                0.000000   0.000000
## Hhold.fctrSKy                                0.000000   0.000000
## Income.fctr.L                                0.000000   0.000000
## Income.fctr^4                                0.000000   0.000000
## Income.fctr^5                                0.000000   0.000000
## Income.fctr^6                                0.000000   0.000000
## Q100010.fctrNo                               0.000000   0.000000
## Q100010.fctrYes                              0.000000   0.000000
## Q100562.fctrYes                              0.000000   0.000000
## Q100680.fctrNo                               0.000000   0.000000
## Q100680.fctrYes                              0.000000   0.000000
## Q100689.fctrNo                               0.000000   0.000000
## Q100689.fctrYes                              0.000000   0.000000
## Q101162.fctrOptimist                         0.000000   0.000000
## Q101162.fctrPessimist                        0.000000   0.000000
## Q101163.fctrMom                              0.000000   0.000000
## Q101596.fctrNo                               0.000000   0.000000
## Q101596.fctrYes                              0.000000   0.000000
## Q102089.fctrOwn                              0.000000   0.000000
## Q102089.fctrRent                             0.000000   0.000000
## Q102289.fctrNo                               0.000000   0.000000
## Q102289.fctrYes                              0.000000   0.000000
## Q102674.fctrNo                               0.000000   0.000000
## Q102674.fctrYes                              0.000000   0.000000
## Q102687.fctrNo                               0.000000   0.000000
## Q102687.fctrYes                              0.000000   0.000000
## Q102906.fctrNo                               0.000000   0.000000
## Q102906.fctrYes                              0.000000   0.000000
## Q103293.fctrNo                               0.000000   0.000000
## Q103293.fctrYes                              0.000000   0.000000
## Q104996.fctrNo                               0.000000   0.000000
## Q104996.fctrYes                              0.000000   0.000000
## Q105655.fctrNo                               0.000000   0.000000
## Q105655.fctrYes                              0.000000   0.000000
## Q105840.fctrNo                               0.000000   0.000000
## Q105840.fctrYes                              0.000000   0.000000
## Q106042.fctrNo                               0.000000   0.000000
## Q106042.fctrYes                              0.000000   0.000000
## Q106272.fctrNo                               0.000000   0.000000
## Q106388.fctrNo                               0.000000   0.000000
## Q106388.fctrYes                              0.000000   0.000000
## Q106389.fctrNo                               0.000000   0.000000
## Q106389.fctrYes                              0.000000   0.000000
## Q106993.fctrNo                               0.000000   0.000000
## Q106993.fctrYes                              0.000000   0.000000
## Q106997.fctrYy                               0.000000   0.000000
## Q107491.fctrNo                               0.000000   0.000000
## Q107491.fctrYes                              0.000000   0.000000
## Q107869.fctrNo                               0.000000   0.000000
## Q107869.fctrYes                              0.000000   0.000000
## Q108342.fctrIn-person                        0.000000   0.000000
## Q108342.fctrOnline                           0.000000   0.000000
## Q108343.fctrNo                               0.000000   0.000000
## Q108343.fctrYes                              0.000000   0.000000
## Q108617.fctrNo                               0.000000   0.000000
## Q108617.fctrYes                              0.000000   0.000000
## Q108754.fctrNo                               0.000000   0.000000
## Q108754.fctrYes                              0.000000   0.000000
## Q108855.fctrUmm...                           0.000000   0.000000
## Q108855.fctrYes!                             0.000000   0.000000
## Q108856.fctrSocialize                        0.000000   0.000000
## Q108856.fctrSpace                            0.000000   0.000000
## Q108950.fctrCautious                         0.000000   0.000000
## Q108950.fctrRisk-friendly                    0.000000   0.000000
## Q109367.fctrNo                               0.000000   0.000000
## Q109367.fctrYes                              0.000000   0.000000
## Q110740.fctrMac                              0.000000   0.000000
## Q111220.fctrNo                               0.000000   0.000000
## Q111220.fctrYes                              0.000000   0.000000
## Q111580.fctrDemanding                        0.000000   0.000000
## Q111580.fctrSupportive                       0.000000   0.000000
## Q111848.fctrNo                               0.000000   0.000000
## Q111848.fctrYes                              0.000000   0.000000
## Q112270.fctrNo                               0.000000   0.000000
## Q112270.fctrYes                              0.000000   0.000000
## Q112478.fctrNo                               0.000000   0.000000
## Q112478.fctrYes                              0.000000   0.000000
## Q112512.fctrNo                               0.000000   0.000000
## Q112512.fctrYes                              0.000000   0.000000
## Q113583.fctrTalk                             0.000000   0.000000
## Q113583.fctrTunes                            0.000000   0.000000
## Q113584.fctrPeople                           0.000000   0.000000
## Q113584.fctrTechnology                       0.000000   0.000000
## Q113992.fctrNo                               0.000000   0.000000
## Q113992.fctrYes                              0.000000   0.000000
## Q114152.fctrNo                               0.000000   0.000000
## Q114152.fctrYes                              0.000000   0.000000
## Q114386.fctrMysterious                       0.000000   0.000000
## Q114386.fctrTMI                              0.000000   0.000000
## Q114517.fctrNo                               0.000000   0.000000
## Q114517.fctrYes                              0.000000   0.000000
## Q114748.fctrNo                               0.000000   0.000000
## Q114748.fctrYes                              0.000000   0.000000
## Q114961.fctrNo                               0.000000   0.000000
## Q114961.fctrYes                              0.000000   0.000000
## Q115195.fctrNo                               0.000000   0.000000
## Q115195.fctrYes                              0.000000   0.000000
## Q115390.fctrNo                               0.000000   0.000000
## Q115390.fctrYes                              0.000000   0.000000
## Q115602.fctrNo                               0.000000   0.000000
## Q115602.fctrYes                              0.000000   0.000000
## Q115610.fctrNo                               0.000000   0.000000
## Q115610.fctrYes                              0.000000   0.000000
## Q115777.fctrEnd                              0.000000   0.000000
## Q115777.fctrStart                            0.000000   0.000000
## Q115899.fctrMe                               0.000000   0.000000
## Q116197.fctrA.M.                             0.000000   0.000000
## Q116197.fctrP.M.                             0.000000   0.000000
## Q116441.fctrNo                               0.000000   0.000000
## Q116441.fctrYes                              0.000000   0.000000
## Q116448.fctrNo                               0.000000   0.000000
## Q116448.fctrYes                              0.000000   0.000000
## Q116601.fctrNo                               0.000000   0.000000
## Q116601.fctrYes                              0.000000   0.000000
## Q116797.fctrNo                               0.000000   0.000000
## Q116797.fctrYes                              0.000000   0.000000
## Q116953.fctrNo                               0.000000   0.000000
## Q116953.fctrYes                              0.000000   0.000000
## Q117186.fctrCool headed                      0.000000   0.000000
## Q117186.fctrHot headed                       0.000000   0.000000
## Q117193.fctrOdd hours                        0.000000   0.000000
## Q117193.fctrStandard hours                   0.000000   0.000000
## Q118117.fctrNo                               0.000000   0.000000
## Q118117.fctrYes                              0.000000   0.000000
## Q118232.fctrId                               0.000000   0.000000
## Q118232.fctrPr                               0.000000   0.000000
## Q118233.fctrNo                               0.000000   0.000000
## Q118233.fctrYes                              0.000000   0.000000
## Q118237.fctrNo                               0.000000   0.000000
## Q118237.fctrYes                              0.000000   0.000000
## Q118892.fctrNo                               0.000000   0.000000
## Q118892.fctrYes                              0.000000   0.000000
## Q119334.fctrNo                               0.000000   0.000000
## Q119334.fctrYes                              0.000000   0.000000
## Q119851.fctrNo                               0.000000   0.000000
## Q119851.fctrYes                              0.000000   0.000000
## Q120012.fctrNo                               0.000000   0.000000
## Q120012.fctrYes                              0.000000   0.000000
## Q120014.fctrNo                               0.000000   0.000000
## Q120014.fctrYes                              0.000000   0.000000
## Q120194.fctrStudy first                      0.000000   0.000000
## Q120194.fctrTry first                        0.000000   0.000000
## Q120379.fctrNo                               0.000000   0.000000
## Q120379.fctrYes                              0.000000   0.000000
## Q120472.fctrArt                              0.000000   0.000000
## Q120472.fctrScience                          0.000000   0.000000
## Q120650.fctrNo                               0.000000   0.000000
## Q120650.fctrYes                              0.000000   0.000000
## Q120978.fctrNo                               0.000000   0.000000
## Q120978.fctrYes                              0.000000   0.000000
## Q121011.fctrNo                               0.000000   0.000000
## Q121011.fctrYes                              0.000000   0.000000
## Q121699.fctrNo                               0.000000   0.000000
## Q121699.fctrYes                              0.000000   0.000000
## Q121700.fctrNo                               0.000000   0.000000
## Q121700.fctrYes                              0.000000   0.000000
## Q122120.fctrNo                               0.000000   0.000000
## Q122120.fctrYes                              0.000000   0.000000
## Q122769.fctrNo                               0.000000   0.000000
## Q122769.fctrYes                              0.000000   0.000000
## Q122770.fctrNo                               0.000000   0.000000
## Q122770.fctrYes                              0.000000   0.000000
## Q122771.fctrPc                               0.000000   0.000000
## Q122771.fctrPt                               0.000000   0.000000
## Q123464.fctrNo                               0.000000   0.000000
## Q123464.fctrYes                              0.000000   0.000000
## Q123621.fctrNo                               0.000000   0.000000
## Q123621.fctrYes                              0.000000   0.000000
## Q124122.fctrNo                               0.000000   0.000000
## Q124122.fctrYes                              0.000000   0.000000
## Q124742.fctrNo                               0.000000   0.000000
## Q124742.fctrYes                              0.000000   0.000000
## Q96024.fctrNo                                0.000000   0.000000
## Q96024.fctrYes                               0.000000   0.000000
## Q98059.fctrOnly-child                        0.000000   0.000000
## Q98059.fctrYes                               0.000000   0.000000
## Q98078.fctrNo                                0.000000   0.000000
## Q98078.fctrYes                               0.000000   0.000000
## Q98197.fctrYes                               0.000000   0.000000
## Q98578.fctrNo                                0.000000   0.000000
## Q98578.fctrYes                               0.000000   0.000000
## Q98869.fctrYes                               0.000000   0.000000
## Q99480.fctrYes                               0.000000   0.000000
## Q99581.fctrNo                                0.000000   0.000000
## Q99581.fctrYes                               0.000000   0.000000
## Q99716.fctrNo                                0.000000   0.000000
## Q99716.fctrYes                               0.000000   0.000000
## Q99982.fctrCheck!                            0.000000   0.000000
## Q99982.fctrNope                              0.000000   0.000000
## YOB.Age.fctr.L                               0.000000   0.000000
## YOB.Age.fctr.Q                               0.000000   0.000000
## YOB.Age.fctr.C                               0.000000   0.000000
## YOB.Age.fctr^4                               0.000000   0.000000
## YOB.Age.fctr^5                               0.000000   0.000000
## YOB.Age.fctr^6                               0.000000   0.000000
## YOB.Age.fctr^7                               0.000000   0.000000
## YOB.Age.fctr^8                               0.000000   0.000000
## Hhold.fctrN:.clusterid.fctr2                 0.000000   0.000000
## Hhold.fctrMKn:.clusterid.fctr2               0.000000   0.000000
## Hhold.fctrMKy:.clusterid.fctr2               0.000000   0.000000
## Hhold.fctrPKn:.clusterid.fctr2               0.000000   0.000000
## Hhold.fctrPKy:.clusterid.fctr2               0.000000   0.000000
## Hhold.fctrSKn:.clusterid.fctr2               0.000000   0.000000
## Hhold.fctrSKy:.clusterid.fctr2               0.000000   0.000000
## Hhold.fctrN:.clusterid.fctr3                 0.000000   0.000000
## Hhold.fctrMKn:.clusterid.fctr3               0.000000   0.000000
## Hhold.fctrMKy:.clusterid.fctr3               0.000000   0.000000
## Hhold.fctrPKn:.clusterid.fctr3               0.000000   0.000000
## Hhold.fctrPKy:.clusterid.fctr3               0.000000   0.000000
## Hhold.fctrSKn:.clusterid.fctr3               0.000000   0.000000
## Hhold.fctrSKy:.clusterid.fctr3               0.000000   0.000000
## Hhold.fctrN:.clusterid.fctr4                 0.000000   0.000000
## Hhold.fctrMKn:.clusterid.fctr4               0.000000   0.000000
## Hhold.fctrMKy:.clusterid.fctr4               0.000000   0.000000
## Hhold.fctrPKn:.clusterid.fctr4               0.000000   0.000000
## Hhold.fctrPKy:.clusterid.fctr4               0.000000   0.000000
## Hhold.fctrSKn:.clusterid.fctr4               0.000000   0.000000
## Hhold.fctrSKy:.clusterid.fctr4               0.000000   0.000000
## YOB.Age.fctrNA:YOB.Age.dff                   0.000000   0.000000
## YOB.Age.fctr(15,20]:YOB.Age.dff              0.000000   0.000000
## YOB.Age.fctr(20,25]:YOB.Age.dff              0.000000   0.000000
## YOB.Age.fctr(25,30]:YOB.Age.dff              0.000000   0.000000
## YOB.Age.fctr(30,35]:YOB.Age.dff              0.000000   0.000000
## YOB.Age.fctr(35,40]:YOB.Age.dff              0.000000   0.000000
## YOB.Age.fctr(40,50]:YOB.Age.dff              0.000000   0.000000
## YOB.Age.fctr(50,65]:YOB.Age.dff              0.000000   0.000000
## YOB.Age.fctr(65,90]:YOB.Age.dff              0.000000   0.000000
## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 108

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1     470          R                         0.4178772
## 2    2565          R                         0.4433796
## 3    1275          R                         0.4443487
## 4     574          R                         0.4477939
## 5     381          R                         0.4483704
## 6    3577          R                         0.4521859
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            D                             TRUE
## 2                            D                             TRUE
## 3                            D                             TRUE
## 4                            D                             TRUE
## 5                            D                             TRUE
## 6                            D                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.5821228                               FALSE
## 2                            0.5566204                               FALSE
## 3                            0.5556513                               FALSE
## 4                            0.5522061                               FALSE
## 5                            0.5516296                               FALSE
## 6                            0.5478141                               FALSE
##   Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                        -0.13212279
## 2                                 FALSE                        -0.10662037
## 3                                 FALSE                        -0.10565126
## 4                                 FALSE                        -0.10220608
## 5                                 FALSE                        -0.10162960
## 6                                 FALSE                        -0.09781408
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 24     2557          R                         0.4751736
## 41     4602          R                         0.4848311
## 47     3566          R                         0.4909901
## 51     4337          R                         0.4969843
## 131    1657          D                         0.5892389
## 166    5783          D                         0.6244881
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 24                             D                             TRUE
## 41                             D                             TRUE
## 47                             D                             TRUE
## 51                             D                             TRUE
## 131                            R                             TRUE
## 166                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 24                             0.5248264
## 41                             0.5151689
## 47                             0.5090099
## 51                             0.5030157
## 131                            0.5892389
## 166                            0.6244881
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 24                                FALSE
## 41                                FALSE
## 47                                FALSE
## 51                                FALSE
## 131                               FALSE
## 166                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 24                                  FALSE
## 41                                  FALSE
## 47                                  FALSE
## 51                                  FALSE
## 131                                 FALSE
## 166                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 24                         -0.07482638
## 41                         -0.06516889
## 47                         -0.05900991
## 51                         -0.05301572
## 131                         0.03923887
## 166                         0.07448809
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 206     483          D                         0.7087168
## 207    1263          D                         0.7105345
## 208     613          D                         0.7141901
## 209    3512          D                         0.7178165
## 210    3978          D                         0.7238014
## 211    4956          D                         0.7303139
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 206                            R                             TRUE
## 207                            R                             TRUE
## 208                            R                             TRUE
## 209                            R                             TRUE
## 210                            R                             TRUE
## 211                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 206                            0.7087168
## 207                            0.7105345
## 208                            0.7141901
## 209                            0.7178165
## 210                            0.7238014
## 211                            0.7303139
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 206                               FALSE
## 207                               FALSE
## 208                               FALSE
## 209                               FALSE
## 210                               FALSE
## 211                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 206                                 FALSE
## 207                                 FALSE
## 208                                 FALSE
## 209                                 FALSE
## 210                                 FALSE
## 211                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 206                          0.1587168
## 207                          0.1605345
## 208                          0.1641901
## 209                          0.1678165
## 210                          0.1738014
## 211                          0.1803139

##     Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy        PKy      6     14      6    0.007153807     0.01195219
## PKn        PKn     13     50     15    0.025549310     0.02589641
## SKy        SKy     23     61     27    0.031170158     0.04581673
## N            N     28    116     33    0.059274400     0.05577689
## MKn        MKn     75    226     94    0.115482882     0.14940239
## SKn        SKn    201    810    252    0.413898825     0.40039841
## MKy        MKy    156    680    195    0.347470618     0.31075697
##     .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy    0.009646302        7.086145        0.5061532     14        3.238914
## PKn    0.024115756       24.399436        0.4879887     50        6.726635
## SKy    0.043408360       28.898436        0.4737449     61       11.815230
## N      0.053054662       54.104346        0.4664168    116       13.604103
## MKn    0.151125402      104.765360        0.4635635    226       35.796619
## SKn    0.405144695      386.381033        0.4770136    810       95.720498
## MKy    0.313504823      308.646219        0.4538915    680       72.981800
##     err.abs.OOB.mean
## PKy        0.5398191
## PKn        0.5174334
## SKy        0.5137057
## N          0.4858608
## MKn        0.4772882
## SKn        0.4762214
## MKy        0.4678321
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##       502.000000      1957.000000       622.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000       914.280975         3.328772 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##      1957.000000       239.883799         3.478161
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 210.854  NA      NA
##         label step_major step_minor label_minor     bgn     end elapsed
## 16 fit.models          7          2           2 203.254 210.864    7.61
## 17 fit.models          7          3           3 210.865      NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn    end
## 17        fit.models          7          3           3 210.865 213.91
## 18 fit.data.training          8          0           0 213.910     NA
##    elapsed
## 17   3.045
## 18      NA

Step 8.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var]))) {    
    warning("Final model same as glbMdlSelId")
    glbMdlFinId <- paste0("Final.", glbMdlSelId)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
    mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
    mdlDf$id <- glbMdlFinId
    glb_models_df <- rbind(glb_models_df, mdlDf)
} else {    
    if (myparseMdlId(glbMdlSelId)$family == "RFE.X") {
        indepVar <- mygetIndepVar(glb_feats_df)
        trnRFEResults <- 
            myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
        if (!isTRUE(all.equal(sort(predictors(trnRFEResults)),
                              sort(predictors(glbRFEResults))))) {
            print("Diffs predictors(trnRFEResults) vs. predictors(glbRFEResults):")
            print(setdiff(predictors(trnRFEResults), predictors(glbRFEResults)))
            print("Diffs predictors(glbRFEResults) vs. predictors(trnRFEResults):")
            print(setdiff(predictors(glbRFEResults), predictors(trnRFEResults)))
        }
    }

    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        mdlIndepVar <- row.names(mdlimp_df)
        if (glb_is_classification)
            mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
                            mygetPredictIds(glb_rsp_var, thsMdlId)$prob  %in% mdlIndepVar)] else
            mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
                            mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlIndepVar)]
        # Fit selected models on glbObsTrn
        for (mdl_id in mdlIdVcr) {
            mdl_id_components <- myparseMdlId(mdl_id)
            mdlIdPfx <- mdl_id_components$family
            # if (grepl("RFE\\.X\\.", mdlIdPfx)) 
            #     mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
            #         predictors(trnRFEResults))) else
                # mdlIndepVars <- trim(unlist(
                #     strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            thsIndepVar <- unlist(
                    strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]"))
            thsSpc <- myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = paste0("Final.", mdlIdPfx), 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = mdl_id_components$resample,
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = mdl_id_components$alg,
                        train.preProcess = mdl_id_components$preProcess))
            ret_lst <- myfit_mdl(mdl_specs_lst = thsSpc,
                    indepVar = thsIndepVar,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = thsSpc$id, 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = thsSpc$id, 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
        
    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        mdlIndepVar <- row.names(mdlimp_df)        
        if (glb_is_classification)
            mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
                            mygetPredictIds(glb_rsp_var, thsMdlId)$prob  %in% mdlIndepVar)] else
            mdlIdVcr <- glbMdlEnsemble[sapply(glbMdlEnsemble, function(thsMdlId)
                            mygetPredictIds(glb_rsp_var, thsMdlId)$value %in% mdlIndepVar)]
        mdlIdVcr <- paste("Final", mdlIdVcr, sep = ".")
        mdlIndepVar <- gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"), mdlIndepVar, fixed = TRUE)
        
        # if (glb_is_classification && glb_is_binomial)
        #     indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
        #                             row.names(mdlimp_df)) else
        #     indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
        #                             row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
        # indepVar <- myextract_actual_feats(predictors(trnRFEResults))
        mdlIndepVar <- myextract_actual_feats(predictors(glbRFEResults))        
    } else mdlIndepVar <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glbMdlSelId
                                                   , "feats"], "[,]")))
        
    # if (!is.null(glbMdlPreprocMethods) &&
    #     ((match_pos <- regexpr(gsub(".", "\\.", 
    #                                 paste(glbMdlPreprocMethods, collapse = "|"),
    #                                fixed = TRUE), glbMdlSelId)) != -1))
    #     ths_preProcess <- str_sub(glbMdlSelId, match_pos, 
    #                             match_pos + attr(match_pos, "match.length") - 1) else
    #     ths_preProcess <- NULL   
    
    # mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
    #                                "Final.Ensemble", "Final")
    thsMdlId <- paste0("Final.", glbMdlSelId)
    thsMdlIdComponents <- myparseMdlId(thsMdlId)
    # mdl_id_pfx <- paste("Final", myparseMdlId(glbMdlSelId)$family, sep = ".")
    mdl_id_pfx <- thsMdlIdComponents$family
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    # method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))

    thsSpc <- myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = mdl_id_pfx, 
        type = glb_model_type, tune.df = glbMdlTuneParams,
        trainControl.method = thsMdlIdComponents$resample,
        trainControl.number = glb_rcv_n_folds,
        trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = thsMdlIdComponents$alg,
        train.preProcess = thsMdlIdComponents$preProcess))

    glbMdlFinId <- thsSpc$id
    if (!(grepl("Ensemble", glbMdlSelId)))
        ret_lst <- myfit_mdl(mdl_specs_lst = thsSpc,
                             indepVar = mdlIndepVar,
                             rsp_var = glb_rsp_var, 
                             fit_df = glbObsTrn, OOB_df = NULL) else {
                                 
        # Final model same as selected model except for the model features
        tmp_models_df <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
        tmp_models_df$id <- paste0("Final.", tmp_models_df$id)
        row.names(tmp_models_df) <- tmp_models_df$id
        tmp_models_df$feats <- gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
                                    tmp_models_df$feats, fixed = TRUE)
        glb_models_df <- rbind(glb_models_df, tmp_models_df)
        
        tmp_fin_mdl <- glb_sel_mdl
        # tmp_fin_mdl$coefnames <- gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
        #                               tmp_fin_mdl$coefnames, fixed = TRUE)
        # dimnames(tmp_fin_mdl$finalModel$beta)[[1]] <- 
        #     gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
        #         dimnames(tmp_fin_mdl$finalModel$beta)[[1]], fixed = TRUE)
        # tmp_fin_mdl$finalModel$xNames <- 
        #     gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
        #         tmp_fin_mdl$finalModel$xNames, fixed = TRUE)
        # 
        # thsAts <- attributes(tmp_fin_mdl$terms)
        # # thsAts$variables <- class == "call" & objects / symbols are stored as a formula
        # thsAts$term.labels <- 
        #     gsub(glb_rsp_var, paste0(glb_rsp_var, ".Final"),
        #         thsAts$term.labels, fixed = TRUE)
        # attributes(tmp_fin_mdl$terms) <- thsAts
        # 
        glb_models_lst[[glbMdlFinId]] <- tmp_fin_mdl
    }
    
    glb_fin_mdl <- glb_models_lst[[glbMdlFinId]] 
}
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "myfit_mdl: fitting model: Final.All.X##rcv#glmnet"
## [1] "    indepVar: Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff"
## [1] "myfit_mdl: setup complete: 0.677000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0299 on full training set
## [1] "myfit_mdl: train complete: 19.109000 secs"

##             Length Class      Mode     
## a0             79  -none-     numeric  
## beta        20540  dgCMatrix  S4       
## df             79  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         79  -none-     numeric  
## dev.ratio      79  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        260  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##                     (Intercept)                   Hhold.fctrMKy 
##                     0.239007070                     0.033507266 
##                   Hhold.fctrPKn                   Hhold.fctrPKy 
##                    -0.227686861                    -0.032591893 
##                   Income.fctr.Q                  Q100562.fctrNo 
##                     0.039333160                    -0.018180749 
##                 Q101163.fctrDad                  Q106272.fctrNo 
##                     0.125067724                    -0.034909617 
##                  Q106997.fctrGr                Q108855.fctrYes! 
##                     0.059926842                     0.037896957 
##                  Q110740.fctrPC                  Q113181.fctrNo 
##                     0.071996630                    -0.176627116 
##                 Q113181.fctrYes                  Q115611.fctrNo 
##                     0.095363634                    -0.088081742 
##                 Q115611.fctrYes                  Q115899.fctrCs 
##                     0.285026222                    -0.003861343 
##               Q116881.fctrHappy               Q116881.fctrRight 
##                    -0.002832559                     0.172117924 
##                   Q98197.fctrNo                   Q98869.fctrNo 
##                    -0.178871629                    -0.118134076 
##                   Q99480.fctrNo  Hhold.fctrPKn:.clusterid.fctr2 
##                    -0.077091582                    -0.266307387 
##  Hhold.fctrMKy:.clusterid.fctr4 YOB.Age.fctr(35,40]:YOB.Age.dff 
##                     0.097377657                    -0.001130053 
## [1] "max lambda < lambdaOpt:"
##                     (Intercept)                   Hhold.fctrMKy 
##                    2.318858e-01                    4.064917e-02 
##                   Hhold.fctrPKn                   Hhold.fctrPKy 
##                   -2.477206e-01                   -9.495962e-02 
##                   Income.fctr.Q                   Income.fctr^6 
##                    5.605138e-02                   -1.102089e-02 
##                  Q100562.fctrNo                 Q100689.fctrYes 
##                   -3.743273e-02                   -5.207831e-05 
##                 Q101163.fctrDad                 Q104996.fctrYes 
##                    1.349664e-01                   -1.801455e-03 
##                  Q106272.fctrNo                  Q106997.fctrGr 
##                   -4.634035e-02                    7.822020e-02 
##                Q108855.fctrYes!                  Q110740.fctrPC 
##                    4.982942e-02                    8.470511e-02 
##                  Q113181.fctrNo                 Q113181.fctrYes 
##                   -1.807621e-01                    9.776058e-02 
##                  Q115611.fctrNo                 Q115611.fctrYes 
##                   -8.738496e-02                    2.953464e-01 
##                  Q115899.fctrCs               Q116881.fctrHappy 
##                   -1.582658e-02                   -1.056389e-02 
##               Q116881.fctrRight                  Q116953.fctrNo 
##                    1.782979e-01                    1.560789e-02 
##                  Q118232.fctrId             Q120472.fctrScience 
##                   -1.082165e-02                    1.377212e-02 
##                  Q122120.fctrNo                   Q98197.fctrNo 
##                   -1.671014e-02                   -1.832513e-01 
##                   Q98869.fctrNo                   Q99480.fctrNo 
##                   -1.326358e-01                   -8.793075e-02 
##                  Q99716.fctrYes  Hhold.fctrPKn:.clusterid.fctr2 
##                   -1.206254e-03                   -3.142328e-01 
##  Hhold.fctrMKy:.clusterid.fctr4 YOB.Age.fctr(35,40]:YOB.Age.dff 
##                    1.144192e-01                   -8.436593e-03 
## [1] "myfit_mdl: train diagnostics complete: 19.758000 secs"

##          Prediction
## Reference    D    R
##         D  502  536
##         R  395 1026
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.213908e-01   2.094839e-01   6.018809e-01   6.406111e-01   5.778772e-01 
## AccuracyPValue  McnemarPValue 
##   6.201681e-06   4.468387e-06 
## [1] "myfit_mdl: predict complete: 26.073000 secs"
##                        id
## 1 Final.All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           feats
## 1 Q115611.fctr,Q113181.fctr,Q98197.fctr,Q116881.fctr,Q108855.fctr,Q106272.fctr,Q122771.fctr,Q123621.fctr,Q106388.fctr,Q110740.fctr,Q122769.fctr,Q120472.fctr,Q101596.fctr,Q119334.fctr,Q114152.fctr,Q98869.fctr,Q115899.fctr,Q116797.fctr,Q118232.fctr,Gender.fctr,Q105655.fctr,Q99480.fctr,Q123464.fctr,Q120650.fctr,Q122120.fctr,Q107869.fctr,Q120014.fctr,Q102289.fctr,Income.fctr,Q122770.fctr,Q111580.fctr,Q116601.fctr,Q117186.fctr,Q106993.fctr,Q112270.fctr,Q101162.fctr,Q108856.fctr,Q117193.fctr,Q116441.fctr,Q119851.fctr,Q111848.fctr,Q98578.fctr,Q118892.fctr,Q114386.fctr,Q120978.fctr,Q112512.fctr,Q102674.fctr,Q96024.fctr,Q108950.fctr,Q115610.fctr,YOB.Age.fctr,Q112478.fctr,Q116197.fctr,Q124742.fctr,Q106389.fctr,Edn.fctr,Q118117.fctr,Q100562.fctr,Q107491.fctr,Q116448.fctr,Q108754.fctr,Q116953.fctr,Q115602.fctr,Q118233.fctr,Q120012.fctr,Q118237.fctr,Q99581.fctr,.rnorm,Q120194.fctr,Q115777.fctr,Q106997.fctr,Q100680.fctr,Q113584.fctr,Q108343.fctr,Q121700.fctr,Q105840.fctr,Q120379.fctr,Q103293.fctr,Q124122.fctr,Q109367.fctr,Q113992.fctr,Q121699.fctr,Q121011.fctr,Q114748.fctr,Q106042.fctr,Q111220.fctr,Q114517.fctr,Q102687.fctr,Q102906.fctr,Q98078.fctr,Q115390.fctr,Q102089.fctr,Q100010.fctr,Q99982.fctr,Q113583.fctr,Q108342.fctr,Q104996.fctr,Q119650.fctr,Q100689.fctr,Q108617.fctr,Q115195.fctr,Q99716.fctr,Q101163.fctr,Q98059.fctr,Q114961.fctr,Hhold.fctr,Hhold.fctr:.clusterid.fctr,YOB.Age.fctr:YOB.Age.dff
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     18.334                 1.773
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.5699133    0.2890173    0.8508093        0.652588
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.55       0.6878981        0.6003805
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6018809             0.6406111     0.1204087
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01449443      0.02968751
## [1] "myfit_mdl: exit: 26.094000 secs"
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
##                label step_major step_minor label_minor     bgn     end
## 18 fit.data.training          8          0           0 213.910 240.508
## 19 fit.data.training          8          1           1 240.509      NA
##    elapsed
## 18  26.598
## 19      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification)
    #     mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    # mdlEnsembleComps <- gsub(paste0("^", 
    #                     gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
    #                          "", mdlEnsembleComps)
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$prob  %in% mdlEnsembleComps)] else
        mdlEnsembleComps <- glb_models_df$id[sapply(glb_models_df$id, function(thsMdlId)
                        mygetPredictIds(glb_rsp_var, thsMdlId)$value  %in% mdlEnsembleComps)]
                        
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        # glb_fin_mdl uses the same coefficients as glb_sel_mdl, 
        #   so copy the "Final" columns into "non-Final" columns
        glbObsTrn[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsTrn[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
        glbObsNew[, gsub("Final.", "", unlist(mygetPredictIds(glb_rsp_var, mdl_id)))] <-
            glbObsNew[, unlist(mygetPredictIds(glb_rsp_var, mdl_id))]
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.55
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##                                 All.X..rcv.glmnet.imp
## Q115611.fctrYes                             52.729697
## Hhold.fctrPKn:.clusterid.fctr2               0.000000
## Hhold.fctrPKn                              100.000000
## Q98197.fctrNo                               40.627886
## Q113181.fctrNo                              51.264404
## Q116881.fctrRight                           23.579086
## Q101163.fctrDad                             27.773431
## Q98869.fctrNo                                5.552206
## Hhold.fctrMKy:.clusterid.fctr4               0.000000
## Q113181.fctrYes                             64.984274
## Q115611.fctrNo                              44.707621
## Q99480.fctrNo                                6.082982
## Q110740.fctrPC                              11.933368
## Q106997.fctrGr                              15.677653
## Hhold.fctrPKy                               17.918579
## Income.fctr.Q                                5.630958
## Q108855.fctrYes!                             0.000000
## Q106272.fctrNo                               0.000000
## Hhold.fctrMKy                               25.424438
## Q100562.fctrNo                               9.122468
## Q115899.fctrCs                               5.392219
## Q122120.fctrNo                               0.000000
## Q116953.fctrNo                               0.000000
## Q120472.fctrScience                          0.000000
## Q116881.fctrHappy                            3.039743
## Income.fctr^6                                0.000000
## Q118232.fctrId                               0.000000
## YOB.Age.fctr(35,40]:YOB.Age.dff              0.000000
## Q104996.fctrYes                              0.000000
## Q99716.fctrYes                               0.000000
## Q100689.fctrYes                              0.000000
## .rnorm                                       0.000000
## Edn.fctr.C                                   0.000000
## Edn.fctr.L                                   0.000000
## Edn.fctr.Q                                   0.000000
## Edn.fctr^4                                   0.000000
## Edn.fctr^5                                   0.000000
## Edn.fctr^6                                   0.000000
## Edn.fctr^7                                   0.000000
## Gender.fctrF                                 0.000000
## Gender.fctrM                                 0.000000
## Hhold.fctrMKn                                0.000000
## Hhold.fctrMKn:.clusterid.fctr2               0.000000
## Hhold.fctrMKn:.clusterid.fctr3               0.000000
## Hhold.fctrMKn:.clusterid.fctr4               0.000000
## Hhold.fctrMKy:.clusterid.fctr2               0.000000
## Hhold.fctrMKy:.clusterid.fctr3               0.000000
## Hhold.fctrN:.clusterid.fctr2                 0.000000
## Hhold.fctrN:.clusterid.fctr3                 0.000000
## Hhold.fctrN:.clusterid.fctr4                 0.000000
## Hhold.fctrPKn:.clusterid.fctr3               0.000000
## Hhold.fctrPKn:.clusterid.fctr4               0.000000
## Hhold.fctrPKy:.clusterid.fctr2               0.000000
## Hhold.fctrPKy:.clusterid.fctr3               0.000000
## Hhold.fctrPKy:.clusterid.fctr4               0.000000
## Hhold.fctrSKn                                0.000000
## Hhold.fctrSKn:.clusterid.fctr2               0.000000
## Hhold.fctrSKn:.clusterid.fctr3               0.000000
## Hhold.fctrSKn:.clusterid.fctr4               0.000000
## Hhold.fctrSKy                                0.000000
## Hhold.fctrSKy:.clusterid.fctr2               0.000000
## Hhold.fctrSKy:.clusterid.fctr3               0.000000
## Hhold.fctrSKy:.clusterid.fctr4               0.000000
## Income.fctr.C                                2.839396
## Income.fctr.L                                0.000000
## Income.fctr^4                                0.000000
## Income.fctr^5                                0.000000
## Q100010.fctrNo                               0.000000
## Q100010.fctrYes                              0.000000
## Q100562.fctrYes                              0.000000
## Q100680.fctrNo                               0.000000
## Q100680.fctrYes                              0.000000
## Q100689.fctrNo                               0.000000
## Q101162.fctrOptimist                         0.000000
## Q101162.fctrPessimist                        0.000000
## Q101163.fctrMom                              0.000000
## Q101596.fctrNo                               0.000000
## Q101596.fctrYes                              0.000000
## Q102089.fctrOwn                              0.000000
## Q102089.fctrRent                             0.000000
## Q102289.fctrNo                               0.000000
## Q102289.fctrYes                              0.000000
## Q102674.fctrNo                               0.000000
## Q102674.fctrYes                              0.000000
## Q102687.fctrNo                               0.000000
## Q102687.fctrYes                              0.000000
## Q102906.fctrNo                               0.000000
## Q102906.fctrYes                              0.000000
## Q103293.fctrNo                               0.000000
## Q103293.fctrYes                              0.000000
## Q104996.fctrNo                               0.000000
## Q105655.fctrNo                               0.000000
## Q105655.fctrYes                              0.000000
## Q105840.fctrNo                               0.000000
## Q105840.fctrYes                              0.000000
## Q106042.fctrNo                               0.000000
## Q106042.fctrYes                              0.000000
## Q106272.fctrYes                             22.437747
## Q106388.fctrNo                               0.000000
## Q106388.fctrYes                              0.000000
## Q106389.fctrNo                               0.000000
## Q106389.fctrYes                              0.000000
## Q106993.fctrNo                               0.000000
## Q106993.fctrYes                              0.000000
## Q106997.fctrYy                               0.000000
## Q107491.fctrNo                               0.000000
## Q107491.fctrYes                              0.000000
## Q107869.fctrNo                               0.000000
## Q107869.fctrYes                              0.000000
## Q108342.fctrIn-person                        0.000000
## Q108342.fctrOnline                           0.000000
## Q108343.fctrNo                               0.000000
## Q108343.fctrYes                              0.000000
## Q108617.fctrNo                               0.000000
## Q108617.fctrYes                              0.000000
## Q108754.fctrNo                               0.000000
## Q108754.fctrYes                              0.000000
## Q108855.fctrUmm...                           0.000000
## Q108856.fctrSocialize                        0.000000
## Q108856.fctrSpace                            0.000000
## Q108950.fctrCautious                         0.000000
## Q108950.fctrRisk-friendly                    0.000000
## Q109367.fctrNo                               0.000000
## Q109367.fctrYes                              0.000000
## Q110740.fctrMac                              0.000000
## Q111220.fctrNo                               0.000000
## Q111220.fctrYes                              0.000000
## Q111580.fctrDemanding                        0.000000
## Q111580.fctrSupportive                       0.000000
## Q111848.fctrNo                               0.000000
## Q111848.fctrYes                              0.000000
## Q112270.fctrNo                               0.000000
## Q112270.fctrYes                              0.000000
## Q112478.fctrNo                               0.000000
## Q112478.fctrYes                              0.000000
## Q112512.fctrNo                               0.000000
## Q112512.fctrYes                              0.000000
## Q113583.fctrTalk                             0.000000
## Q113583.fctrTunes                            0.000000
## Q113584.fctrPeople                           0.000000
## Q113584.fctrTechnology                       0.000000
## Q113992.fctrNo                               0.000000
## Q113992.fctrYes                              0.000000
## Q114152.fctrNo                               0.000000
## Q114152.fctrYes                              0.000000
## Q114386.fctrMysterious                       0.000000
## Q114386.fctrTMI                              0.000000
## Q114517.fctrNo                               0.000000
## Q114517.fctrYes                              0.000000
## Q114748.fctrNo                               0.000000
## Q114748.fctrYes                              0.000000
## Q114961.fctrNo                               0.000000
## Q114961.fctrYes                              0.000000
## Q115195.fctrNo                               0.000000
## Q115195.fctrYes                              0.000000
## Q115390.fctrNo                               0.000000
## Q115390.fctrYes                              0.000000
## Q115602.fctrNo                               0.000000
## Q115602.fctrYes                              0.000000
## Q115610.fctrNo                               0.000000
## Q115610.fctrYes                              0.000000
## Q115777.fctrEnd                              0.000000
## Q115777.fctrStart                            0.000000
## Q115899.fctrMe                               0.000000
## Q116197.fctrA.M.                             0.000000
## Q116197.fctrP.M.                             0.000000
## Q116441.fctrNo                               0.000000
## Q116441.fctrYes                              0.000000
## Q116448.fctrNo                               0.000000
## Q116448.fctrYes                              0.000000
## Q116601.fctrNo                               0.000000
## Q116601.fctrYes                              0.000000
## Q116797.fctrNo                               0.000000
## Q116797.fctrYes                              0.000000
## Q116953.fctrYes                              0.000000
## Q117186.fctrCool headed                      0.000000
## Q117186.fctrHot headed                       0.000000
## Q117193.fctrOdd hours                        0.000000
## Q117193.fctrStandard hours                   0.000000
## Q118117.fctrNo                               0.000000
## Q118117.fctrYes                              0.000000
## Q118232.fctrPr                               0.000000
## Q118233.fctrNo                               0.000000
## Q118233.fctrYes                              0.000000
## Q118237.fctrNo                               0.000000
## Q118237.fctrYes                              0.000000
## Q118892.fctrNo                               0.000000
## Q118892.fctrYes                              0.000000
## Q119334.fctrNo                               0.000000
## Q119334.fctrYes                              0.000000
## Q119650.fctrGiving                           5.596421
## Q119650.fctrReceiving                        1.557860
## Q119851.fctrNo                               0.000000
## Q119851.fctrYes                              0.000000
## Q120012.fctrNo                               0.000000
## Q120012.fctrYes                              0.000000
## Q120014.fctrNo                               0.000000
## Q120014.fctrYes                              0.000000
## Q120194.fctrStudy first                      0.000000
## Q120194.fctrTry first                        0.000000
## Q120379.fctrNo                               0.000000
## Q120379.fctrYes                              0.000000
## Q120472.fctrArt                              0.000000
## Q120650.fctrNo                               0.000000
## Q120650.fctrYes                              0.000000
## Q120978.fctrNo                               0.000000
## Q120978.fctrYes                              0.000000
## Q121011.fctrNo                               0.000000
## Q121011.fctrYes                              0.000000
## Q121699.fctrNo                               0.000000
## Q121699.fctrYes                              0.000000
## Q121700.fctrNo                               0.000000
## Q121700.fctrYes                              0.000000
## Q122120.fctrYes                              0.000000
## Q122769.fctrNo                               0.000000
## Q122769.fctrYes                              0.000000
## Q122770.fctrNo                               0.000000
## Q122770.fctrYes                              0.000000
## Q122771.fctrPc                               0.000000
## Q122771.fctrPt                               0.000000
## Q123464.fctrNo                               0.000000
## Q123464.fctrYes                              0.000000
## Q123621.fctrNo                               0.000000
## Q123621.fctrYes                              0.000000
## Q124122.fctrNo                               0.000000
## Q124122.fctrYes                              0.000000
## Q124742.fctrNo                               0.000000
## Q124742.fctrYes                              0.000000
## Q96024.fctrNo                                0.000000
## Q96024.fctrYes                               0.000000
## Q98059.fctrOnly-child                        0.000000
## Q98059.fctrYes                               0.000000
## Q98078.fctrNo                                0.000000
## Q98078.fctrYes                               0.000000
## Q98197.fctrYes                               0.000000
## Q98578.fctrNo                                0.000000
## Q98578.fctrYes                               0.000000
## Q98869.fctrYes                               0.000000
## Q99480.fctrYes                               0.000000
## Q99581.fctrNo                                0.000000
## Q99581.fctrYes                               0.000000
## Q99716.fctrNo                                0.000000
## Q99982.fctrCheck!                            0.000000
## Q99982.fctrNope                              0.000000
## YOB.Age.fctr(15,20]:YOB.Age.dff              0.000000
## YOB.Age.fctr(20,25]:YOB.Age.dff              0.000000
## YOB.Age.fctr(25,30]:YOB.Age.dff              0.000000
## YOB.Age.fctr(30,35]:YOB.Age.dff              0.000000
## YOB.Age.fctr(40,50]:YOB.Age.dff              0.000000
## YOB.Age.fctr(50,65]:YOB.Age.dff              0.000000
## YOB.Age.fctr(65,90]:YOB.Age.dff              0.000000
## YOB.Age.fctr.C                               0.000000
## YOB.Age.fctr.L                               0.000000
## YOB.Age.fctr.Q                               0.000000
## YOB.Age.fctrNA:YOB.Age.dff                   0.000000
## YOB.Age.fctr^4                               0.000000
## YOB.Age.fctr^5                               0.000000
## YOB.Age.fctr^6                               0.000000
## YOB.Age.fctr^7                               0.000000
## YOB.Age.fctr^8                               0.000000
##                                 Final.All.X..rcv.glmnet.imp          imp
## Q115611.fctrYes                                1.000000e+02 1.000000e+02
## Hhold.fctrPKn:.clusterid.fctr2                 9.986214e+01 9.986214e+01
## Hhold.fctrPKn                                  8.186284e+01 8.186284e+01
## Q98197.fctrNo                                  6.240403e+01 6.240403e+01
## Q113181.fctrNo                                 6.158911e+01 6.158911e+01
## Q116881.fctrRight                              6.037796e+01 6.037796e+01
## Q101163.fctrDad                                4.478129e+01 4.478129e+01
## Q98869.fctrNo                                  4.316389e+01 4.316389e+01
## Hhold.fctrMKy:.clusterid.fctr4                 3.643439e+01 3.643439e+01
## Q113181.fctrYes                                3.328050e+01 3.328050e+01
## Q115611.fctrNo                                 3.025038e+01 3.025038e+01
## Q99480.fctrNo                                  2.839881e+01 2.839881e+01
## Q110740.fctrPC                                 2.695620e+01 2.695620e+01
## Q106997.fctrGr                                 2.373294e+01 2.373294e+01
## Hhold.fctrPKy                                  2.171103e+01 2.171103e+01
## Income.fctr.Q                                  1.636844e+01 1.636844e+01
## Q108855.fctrYes!                               1.506953e+01 1.506953e+01
## Q106272.fctrNo                                 1.395534e+01 1.395534e+01
## Hhold.fctrMKy                                  1.275156e+01 1.275156e+01
## Q100562.fctrNo                                 9.501393e+00 9.501393e+00
## Q115899.fctrCs                                 3.340786e+00 3.340786e+00
## Q122120.fctrNo                                 2.806431e+00 2.806431e+00
## Q116953.fctrNo                                 2.621310e+00 2.621310e+00
## Q120472.fctrScience                            2.312996e+00 2.312996e+00
## Q116881.fctrHappy                              2.275023e+00 2.275023e+00
## Income.fctr^6                                  1.850934e+00 1.850934e+00
## Q118232.fctrId                                 1.817472e+00 1.817472e+00
## YOB.Age.fctr(35,40]:YOB.Age.dff                1.616719e+00 1.616719e+00
## Q104996.fctrYes                                3.025503e-01 3.025503e-01
## Q99716.fctrYes                                 2.025877e-01 2.025877e-01
## Q100689.fctrYes                                8.746435e-03 8.746435e-03
## .rnorm                                         0.000000e+00 0.000000e+00
## Edn.fctr.C                                     0.000000e+00 0.000000e+00
## Edn.fctr.L                                     0.000000e+00 0.000000e+00
## Edn.fctr.Q                                     0.000000e+00 0.000000e+00
## Edn.fctr^4                                     0.000000e+00 0.000000e+00
## Edn.fctr^5                                     0.000000e+00 0.000000e+00
## Edn.fctr^6                                     0.000000e+00 0.000000e+00
## Edn.fctr^7                                     0.000000e+00 0.000000e+00
## Gender.fctrF                                   0.000000e+00 0.000000e+00
## Gender.fctrM                                   0.000000e+00 0.000000e+00
## Hhold.fctrMKn                                  0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr2                 0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr3                 0.000000e+00 0.000000e+00
## Hhold.fctrMKn:.clusterid.fctr4                 0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr2                 0.000000e+00 0.000000e+00
## Hhold.fctrMKy:.clusterid.fctr3                 0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr2                   0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr3                   0.000000e+00 0.000000e+00
## Hhold.fctrN:.clusterid.fctr4                   0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr3                 0.000000e+00 0.000000e+00
## Hhold.fctrPKn:.clusterid.fctr4                 0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr2                 0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr3                 0.000000e+00 0.000000e+00
## Hhold.fctrPKy:.clusterid.fctr4                 0.000000e+00 0.000000e+00
## Hhold.fctrSKn                                  0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr2                 0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr3                 0.000000e+00 0.000000e+00
## Hhold.fctrSKn:.clusterid.fctr4                 0.000000e+00 0.000000e+00
## Hhold.fctrSKy                                  0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr2                 0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr3                 0.000000e+00 0.000000e+00
## Hhold.fctrSKy:.clusterid.fctr4                 0.000000e+00 0.000000e+00
## Income.fctr.C                                  0.000000e+00 0.000000e+00
## Income.fctr.L                                  0.000000e+00 0.000000e+00
## Income.fctr^4                                  0.000000e+00 0.000000e+00
## Income.fctr^5                                  0.000000e+00 0.000000e+00
## Q100010.fctrNo                                 0.000000e+00 0.000000e+00
## Q100010.fctrYes                                0.000000e+00 0.000000e+00
## Q100562.fctrYes                                0.000000e+00 0.000000e+00
## Q100680.fctrNo                                 0.000000e+00 0.000000e+00
## Q100680.fctrYes                                0.000000e+00 0.000000e+00
## Q100689.fctrNo                                 0.000000e+00 0.000000e+00
## Q101162.fctrOptimist                           0.000000e+00 0.000000e+00
## Q101162.fctrPessimist                          0.000000e+00 0.000000e+00
## Q101163.fctrMom                                0.000000e+00 0.000000e+00
## Q101596.fctrNo                                 0.000000e+00 0.000000e+00
## Q101596.fctrYes                                0.000000e+00 0.000000e+00
## Q102089.fctrOwn                                0.000000e+00 0.000000e+00
## Q102089.fctrRent                               0.000000e+00 0.000000e+00
## Q102289.fctrNo                                 0.000000e+00 0.000000e+00
## Q102289.fctrYes                                0.000000e+00 0.000000e+00
## Q102674.fctrNo                                 0.000000e+00 0.000000e+00
## Q102674.fctrYes                                0.000000e+00 0.000000e+00
## Q102687.fctrNo                                 0.000000e+00 0.000000e+00
## Q102687.fctrYes                                0.000000e+00 0.000000e+00
## Q102906.fctrNo                                 0.000000e+00 0.000000e+00
## Q102906.fctrYes                                0.000000e+00 0.000000e+00
## Q103293.fctrNo                                 0.000000e+00 0.000000e+00
## Q103293.fctrYes                                0.000000e+00 0.000000e+00
## Q104996.fctrNo                                 0.000000e+00 0.000000e+00
## Q105655.fctrNo                                 0.000000e+00 0.000000e+00
## Q105655.fctrYes                                0.000000e+00 0.000000e+00
## Q105840.fctrNo                                 0.000000e+00 0.000000e+00
## Q105840.fctrYes                                0.000000e+00 0.000000e+00
## Q106042.fctrNo                                 0.000000e+00 0.000000e+00
## Q106042.fctrYes                                0.000000e+00 0.000000e+00
## Q106272.fctrYes                                0.000000e+00 0.000000e+00
## Q106388.fctrNo                                 0.000000e+00 0.000000e+00
## Q106388.fctrYes                                0.000000e+00 0.000000e+00
## Q106389.fctrNo                                 0.000000e+00 0.000000e+00
## Q106389.fctrYes                                0.000000e+00 0.000000e+00
## Q106993.fctrNo                                 0.000000e+00 0.000000e+00
## Q106993.fctrYes                                0.000000e+00 0.000000e+00
## Q106997.fctrYy                                 0.000000e+00 0.000000e+00
## Q107491.fctrNo                                 0.000000e+00 0.000000e+00
## Q107491.fctrYes                                0.000000e+00 0.000000e+00
## Q107869.fctrNo                                 0.000000e+00 0.000000e+00
## Q107869.fctrYes                                0.000000e+00 0.000000e+00
## Q108342.fctrIn-person                          0.000000e+00 0.000000e+00
## Q108342.fctrOnline                             0.000000e+00 0.000000e+00
## Q108343.fctrNo                                 0.000000e+00 0.000000e+00
## Q108343.fctrYes                                0.000000e+00 0.000000e+00
## Q108617.fctrNo                                 0.000000e+00 0.000000e+00
## Q108617.fctrYes                                0.000000e+00 0.000000e+00
## Q108754.fctrNo                                 0.000000e+00 0.000000e+00
## Q108754.fctrYes                                0.000000e+00 0.000000e+00
## Q108855.fctrUmm...                             0.000000e+00 0.000000e+00
## Q108856.fctrSocialize                          0.000000e+00 0.000000e+00
## Q108856.fctrSpace                              0.000000e+00 0.000000e+00
## Q108950.fctrCautious                           0.000000e+00 0.000000e+00
## Q108950.fctrRisk-friendly                      0.000000e+00 0.000000e+00
## Q109367.fctrNo                                 0.000000e+00 0.000000e+00
## Q109367.fctrYes                                0.000000e+00 0.000000e+00
## Q110740.fctrMac                                0.000000e+00 0.000000e+00
## Q111220.fctrNo                                 0.000000e+00 0.000000e+00
## Q111220.fctrYes                                0.000000e+00 0.000000e+00
## Q111580.fctrDemanding                          0.000000e+00 0.000000e+00
## Q111580.fctrSupportive                         0.000000e+00 0.000000e+00
## Q111848.fctrNo                                 0.000000e+00 0.000000e+00
## Q111848.fctrYes                                0.000000e+00 0.000000e+00
## Q112270.fctrNo                                 0.000000e+00 0.000000e+00
## Q112270.fctrYes                                0.000000e+00 0.000000e+00
## Q112478.fctrNo                                 0.000000e+00 0.000000e+00
## Q112478.fctrYes                                0.000000e+00 0.000000e+00
## Q112512.fctrNo                                 0.000000e+00 0.000000e+00
## Q112512.fctrYes                                0.000000e+00 0.000000e+00
## Q113583.fctrTalk                               0.000000e+00 0.000000e+00
## Q113583.fctrTunes                              0.000000e+00 0.000000e+00
## Q113584.fctrPeople                             0.000000e+00 0.000000e+00
## Q113584.fctrTechnology                         0.000000e+00 0.000000e+00
## Q113992.fctrNo                                 0.000000e+00 0.000000e+00
## Q113992.fctrYes                                0.000000e+00 0.000000e+00
## Q114152.fctrNo                                 0.000000e+00 0.000000e+00
## Q114152.fctrYes                                0.000000e+00 0.000000e+00
## Q114386.fctrMysterious                         0.000000e+00 0.000000e+00
## Q114386.fctrTMI                                0.000000e+00 0.000000e+00
## Q114517.fctrNo                                 0.000000e+00 0.000000e+00
## Q114517.fctrYes                                0.000000e+00 0.000000e+00
## Q114748.fctrNo                                 0.000000e+00 0.000000e+00
## Q114748.fctrYes                                0.000000e+00 0.000000e+00
## Q114961.fctrNo                                 0.000000e+00 0.000000e+00
## Q114961.fctrYes                                0.000000e+00 0.000000e+00
## Q115195.fctrNo                                 0.000000e+00 0.000000e+00
## Q115195.fctrYes                                0.000000e+00 0.000000e+00
## Q115390.fctrNo                                 0.000000e+00 0.000000e+00
## Q115390.fctrYes                                0.000000e+00 0.000000e+00
## Q115602.fctrNo                                 0.000000e+00 0.000000e+00
## Q115602.fctrYes                                0.000000e+00 0.000000e+00
## Q115610.fctrNo                                 0.000000e+00 0.000000e+00
## Q115610.fctrYes                                0.000000e+00 0.000000e+00
## Q115777.fctrEnd                                0.000000e+00 0.000000e+00
## Q115777.fctrStart                              0.000000e+00 0.000000e+00
## Q115899.fctrMe                                 0.000000e+00 0.000000e+00
## Q116197.fctrA.M.                               0.000000e+00 0.000000e+00
## Q116197.fctrP.M.                               0.000000e+00 0.000000e+00
## Q116441.fctrNo                                 0.000000e+00 0.000000e+00
## Q116441.fctrYes                                0.000000e+00 0.000000e+00
## Q116448.fctrNo                                 0.000000e+00 0.000000e+00
## Q116448.fctrYes                                0.000000e+00 0.000000e+00
## Q116601.fctrNo                                 0.000000e+00 0.000000e+00
## Q116601.fctrYes                                0.000000e+00 0.000000e+00
## Q116797.fctrNo                                 0.000000e+00 0.000000e+00
## Q116797.fctrYes                                0.000000e+00 0.000000e+00
## Q116953.fctrYes                                0.000000e+00 0.000000e+00
## Q117186.fctrCool headed                        0.000000e+00 0.000000e+00
## Q117186.fctrHot headed                         0.000000e+00 0.000000e+00
## Q117193.fctrOdd hours                          0.000000e+00 0.000000e+00
## Q117193.fctrStandard hours                     0.000000e+00 0.000000e+00
## Q118117.fctrNo                                 0.000000e+00 0.000000e+00
## Q118117.fctrYes                                0.000000e+00 0.000000e+00
## Q118232.fctrPr                                 0.000000e+00 0.000000e+00
## Q118233.fctrNo                                 0.000000e+00 0.000000e+00
## Q118233.fctrYes                                0.000000e+00 0.000000e+00
## Q118237.fctrNo                                 0.000000e+00 0.000000e+00
## Q118237.fctrYes                                0.000000e+00 0.000000e+00
## Q118892.fctrNo                                 0.000000e+00 0.000000e+00
## Q118892.fctrYes                                0.000000e+00 0.000000e+00
## Q119334.fctrNo                                 0.000000e+00 0.000000e+00
## Q119334.fctrYes                                0.000000e+00 0.000000e+00
## Q119650.fctrGiving                             0.000000e+00 0.000000e+00
## Q119650.fctrReceiving                          0.000000e+00 0.000000e+00
## Q119851.fctrNo                                 0.000000e+00 0.000000e+00
## Q119851.fctrYes                                0.000000e+00 0.000000e+00
## Q120012.fctrNo                                 0.000000e+00 0.000000e+00
## Q120012.fctrYes                                0.000000e+00 0.000000e+00
## Q120014.fctrNo                                 0.000000e+00 0.000000e+00
## Q120014.fctrYes                                0.000000e+00 0.000000e+00
## Q120194.fctrStudy first                        0.000000e+00 0.000000e+00
## Q120194.fctrTry first                          0.000000e+00 0.000000e+00
## Q120379.fctrNo                                 0.000000e+00 0.000000e+00
## Q120379.fctrYes                                0.000000e+00 0.000000e+00
## Q120472.fctrArt                                0.000000e+00 0.000000e+00
## Q120650.fctrNo                                 0.000000e+00 0.000000e+00
## Q120650.fctrYes                                0.000000e+00 0.000000e+00
## Q120978.fctrNo                                 0.000000e+00 0.000000e+00
## Q120978.fctrYes                                0.000000e+00 0.000000e+00
## Q121011.fctrNo                                 0.000000e+00 0.000000e+00
## Q121011.fctrYes                                0.000000e+00 0.000000e+00
## Q121699.fctrNo                                 0.000000e+00 0.000000e+00
## Q121699.fctrYes                                0.000000e+00 0.000000e+00
## Q121700.fctrNo                                 0.000000e+00 0.000000e+00
## Q121700.fctrYes                                0.000000e+00 0.000000e+00
## Q122120.fctrYes                                0.000000e+00 0.000000e+00
## Q122769.fctrNo                                 0.000000e+00 0.000000e+00
## Q122769.fctrYes                                0.000000e+00 0.000000e+00
## Q122770.fctrNo                                 0.000000e+00 0.000000e+00
## Q122770.fctrYes                                0.000000e+00 0.000000e+00
## Q122771.fctrPc                                 0.000000e+00 0.000000e+00
## Q122771.fctrPt                                 0.000000e+00 0.000000e+00
## Q123464.fctrNo                                 0.000000e+00 0.000000e+00
## Q123464.fctrYes                                0.000000e+00 0.000000e+00
## Q123621.fctrNo                                 0.000000e+00 0.000000e+00
## Q123621.fctrYes                                0.000000e+00 0.000000e+00
## Q124122.fctrNo                                 0.000000e+00 0.000000e+00
## Q124122.fctrYes                                0.000000e+00 0.000000e+00
## Q124742.fctrNo                                 0.000000e+00 0.000000e+00
## Q124742.fctrYes                                0.000000e+00 0.000000e+00
## Q96024.fctrNo                                  0.000000e+00 0.000000e+00
## Q96024.fctrYes                                 0.000000e+00 0.000000e+00
## Q98059.fctrOnly-child                          0.000000e+00 0.000000e+00
## Q98059.fctrYes                                 0.000000e+00 0.000000e+00
## Q98078.fctrNo                                  0.000000e+00 0.000000e+00
## Q98078.fctrYes                                 0.000000e+00 0.000000e+00
## Q98197.fctrYes                                 0.000000e+00 0.000000e+00
## Q98578.fctrNo                                  0.000000e+00 0.000000e+00
## Q98578.fctrYes                                 0.000000e+00 0.000000e+00
## Q98869.fctrYes                                 0.000000e+00 0.000000e+00
## Q99480.fctrYes                                 0.000000e+00 0.000000e+00
## Q99581.fctrNo                                  0.000000e+00 0.000000e+00
## Q99581.fctrYes                                 0.000000e+00 0.000000e+00
## Q99716.fctrNo                                  0.000000e+00 0.000000e+00
## Q99982.fctrCheck!                              0.000000e+00 0.000000e+00
## Q99982.fctrNope                                0.000000e+00 0.000000e+00
## YOB.Age.fctr(15,20]:YOB.Age.dff                0.000000e+00 0.000000e+00
## YOB.Age.fctr(20,25]:YOB.Age.dff                0.000000e+00 0.000000e+00
## YOB.Age.fctr(25,30]:YOB.Age.dff                0.000000e+00 0.000000e+00
## YOB.Age.fctr(30,35]:YOB.Age.dff                0.000000e+00 0.000000e+00
## YOB.Age.fctr(40,50]:YOB.Age.dff                0.000000e+00 0.000000e+00
## YOB.Age.fctr(50,65]:YOB.Age.dff                0.000000e+00 0.000000e+00
## YOB.Age.fctr(65,90]:YOB.Age.dff                0.000000e+00 0.000000e+00
## YOB.Age.fctr.C                                 0.000000e+00 0.000000e+00
## YOB.Age.fctr.L                                 0.000000e+00 0.000000e+00
## YOB.Age.fctr.Q                                 0.000000e+00 0.000000e+00
## YOB.Age.fctrNA:YOB.Age.dff                     0.000000e+00 0.000000e+00
## YOB.Age.fctr^4                                 0.000000e+00 0.000000e+00
## YOB.Age.fctr^5                                 0.000000e+00 0.000000e+00
## YOB.Age.fctr^6                                 0.000000e+00 0.000000e+00
## YOB.Age.fctr^7                                 0.000000e+00 0.000000e+00
## YOB.Age.fctr^8                                 0.000000e+00 0.000000e+00
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    5879          R                         0.3800821
## 2      26          R                         0.3768542
## 3    1610          R                         0.4077182
## 4     403          R                         0.4329510
## 5     895          R                         0.4374456
## 6     660          R                         0.4670456
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            D                             TRUE
## 2                            D                             TRUE
## 3                            D                             TRUE
## 4                            D                             TRUE
## 5                            D                             TRUE
## 6                            D                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.6199179                               FALSE
## 2                            0.6231458                               FALSE
## 3                            0.5922818                               FALSE
## 4                            0.5670490                               FALSE
## 5                            0.5625544                               FALSE
## 6                            0.5329544                               FALSE
##   Party.fctr.Final.All.X..rcv.glmnet.prob
## 1                               0.3859256
## 2                               0.3890654
## 3                               0.3932298
## 4                               0.3947726
## 5                               0.4064961
## 6                               0.4076968
##   Party.fctr.Final.All.X..rcv.glmnet
## 1                                  D
## 2                                  D
## 3                                  D
## 4                                  D
## 5                                  D
## 6                                  D
##   Party.fctr.Final.All.X..rcv.glmnet.err
## 1                                   TRUE
## 2                                   TRUE
## 3                                   TRUE
## 4                                   TRUE
## 5                                   TRUE
## 6                                   TRUE
##   Party.fctr.Final.All.X..rcv.glmnet.err.abs
## 1                                  0.6140744
## 2                                  0.6109346
## 3                                  0.6067702
## 4                                  0.6052274
## 5                                  0.5935039
## 6                                  0.5923032
##   Party.fctr.Final.All.X..rcv.glmnet.is.acc
## 1                                     FALSE
## 2                                     FALSE
## 3                                     FALSE
## 4                                     FALSE
## 5                                     FALSE
## 6                                     FALSE
##   Party.fctr.Final.All.X..rcv.glmnet.accurate
## 1                                       FALSE
## 2                                       FALSE
## 3                                       FALSE
## 4                                       FALSE
## 5                                       FALSE
## 6                                       FALSE
##   Party.fctr.Final.All.X..rcv.glmnet.error
## 1                               -0.1640744
## 2                               -0.1609346
## 3                               -0.1567702
## 4                               -0.1552274
## 5                               -0.1435039
## 6                               -0.1423032
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 21     1275          R                                NA
## 38     5089          R                         0.4501006
## 446    6011          D                                NA
## 474    1769          D                         0.5826074
## 535    1182          D                         0.5892904
## 916    5452          D                         0.7010340
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 21                          <NA>                               NA
## 38                             D                             TRUE
## 446                         <NA>                               NA
## 474                            R                             TRUE
## 535                            R                             TRUE
## 916                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 21                                    NA
## 38                             0.5498994
## 446                                   NA
## 474                            0.5826074
## 535                            0.5892904
## 916                            0.7010340
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 21                                   NA
## 38                                FALSE
## 446                                  NA
## 474                               FALSE
## 535                               FALSE
## 916                               FALSE
##     Party.fctr.Final.All.X..rcv.glmnet.prob
## 21                                0.4300139
## 38                                0.4427751
## 446                               0.5581125
## 474                               0.5629623
## 535                               0.5748162
## 916                               0.7215536
##     Party.fctr.Final.All.X..rcv.glmnet
## 21                                   D
## 38                                   D
## 446                                  R
## 474                                  R
## 535                                  R
## 916                                  R
##     Party.fctr.Final.All.X..rcv.glmnet.err
## 21                                    TRUE
## 38                                    TRUE
## 446                                   TRUE
## 474                                   TRUE
## 535                                   TRUE
## 916                                   TRUE
##     Party.fctr.Final.All.X..rcv.glmnet.err.abs
## 21                                   0.5699861
## 38                                   0.5572249
## 446                                  0.5581125
## 474                                  0.5629623
## 535                                  0.5748162
## 916                                  0.7215536
##     Party.fctr.Final.All.X..rcv.glmnet.is.acc
## 21                                      FALSE
## 38                                      FALSE
## 446                                     FALSE
## 474                                     FALSE
## 535                                     FALSE
## 916                                     FALSE
##     Party.fctr.Final.All.X..rcv.glmnet.accurate
## 21                                        FALSE
## 38                                        FALSE
## 446                                       FALSE
## 474                                       FALSE
## 535                                       FALSE
## 916                                       FALSE
##     Party.fctr.Final.All.X..rcv.glmnet.error
## 21                              -0.119986086
## 38                              -0.107224923
## 446                              0.008112456
## 474                              0.012962265
## 535                              0.024816175
## 916                              0.171553560
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 926    2641          D                         0.7193139
## 927    4956          D                                NA
## 928    3474          D                         0.7270990
## 929      78          D                         0.7414410
## 930    1309          D                         0.7418801
## 931    3578          D                         0.7371386
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 926                            R                             TRUE
## 927                         <NA>                               NA
## 928                            R                             TRUE
## 929                            R                             TRUE
## 930                            R                             TRUE
## 931                            R                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 926                            0.7193139
## 927                                   NA
## 928                            0.7270990
## 929                            0.7414410
## 930                            0.7418801
## 931                            0.7371386
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 926                               FALSE
## 927                                  NA
## 928                               FALSE
## 929                               FALSE
## 930                               FALSE
## 931                               FALSE
##     Party.fctr.Final.All.X..rcv.glmnet.prob
## 926                               0.7325864
## 927                               0.7408101
## 928                               0.7410468
## 929                               0.7411917
## 930                               0.7522748
## 931                               0.7529558
##     Party.fctr.Final.All.X..rcv.glmnet
## 926                                  R
## 927                                  R
## 928                                  R
## 929                                  R
## 930                                  R
## 931                                  R
##     Party.fctr.Final.All.X..rcv.glmnet.err
## 926                                   TRUE
## 927                                   TRUE
## 928                                   TRUE
## 929                                   TRUE
## 930                                   TRUE
## 931                                   TRUE
##     Party.fctr.Final.All.X..rcv.glmnet.err.abs
## 926                                  0.7325864
## 927                                  0.7408101
## 928                                  0.7410468
## 929                                  0.7411917
## 930                                  0.7522748
## 931                                  0.7529558
##     Party.fctr.Final.All.X..rcv.glmnet.is.acc
## 926                                     FALSE
## 927                                     FALSE
## 928                                     FALSE
## 929                                     FALSE
## 930                                     FALSE
## 931                                     FALSE
##     Party.fctr.Final.All.X..rcv.glmnet.accurate
## 926                                       FALSE
## 927                                       FALSE
## 928                                       FALSE
## 929                                       FALSE
## 930                                       FALSE
## 931                                       FALSE
##     Party.fctr.Final.All.X..rcv.glmnet.error
## 926                                0.1825864
## 927                                0.1908101
## 928                                0.1910468
## 929                                0.1911917
## 930                                0.2022748
## 931                                0.2029558

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "Party.fctr.Final.All.X..rcv.glmnet.prob"   
## [2] "Party.fctr.Final.All.X..rcv.glmnet"        
## [3] "Party.fctr.Final.All.X..rcv.glmnet.err"    
## [4] "Party.fctr.Final.All.X..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final.All.X..rcv.glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  data.training.all 
## 1.0000    1   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.new 
## 2.0000    2   1 1 1 0 
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction   firing:  model.selected 
## 3.0000    3   0 2 1 0 
## 3.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  data.training.all.prediction 
## 4.0000    5   0 1 1 1 
## 4.0000   multiple enabled transitions:  model.final data.training.all.prediction data.new.prediction     firing:  model.final 
## 5.0000    4   0 0 2 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 19 fit.data.training          8          1           1 240.509 247.589
## 20  predict.data.new          9          0           0 247.589      NA
##    elapsed
## 19    7.08
## 20      NA

Step 9.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.55

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.55
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 108
## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## Warning: Removed 622 rows containing missing values (geom_point).

## NULL
## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"
## [1] "Stacking file Votes_Ensemble_cnk06_out_fin.csv to prediction outputs..."
## [1] 0.55
## [1] "glbMdlSelId: All.X##rcv#glmnet"
## [1] "glbMdlFinId: Final.All.X##rcv#glmnet"
## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet 
##                          0
##                            max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glmnet                 0.5796813       0.5801856
## Low.cor.X##rcv#glmnet             0.5737052       0.5816378
## Max.cor.Y.rcv.1X1###glmnet        0.5737052       0.5592306
## Max.cor.Y##rcv#rpart              0.5737052       0.5325318
## Random###myrandom_classfr         0.5737052       0.5181075
## MFO###myMFO_classfr               0.5737052       0.5000000
## Final.All.X##rcv#glmnet                  NA              NA
##                            max.AUCpROC.OOB min.elapsedtime.everything
## All.X##rcv#glmnet                0.5067173                     15.750
## Low.cor.X##rcv#glmnet            0.5091186                     15.497
## Max.cor.Y.rcv.1X1###glmnet       0.5338785                      0.721
## Max.cor.Y##rcv#rpart             0.5334729                      1.415
## Random###myrandom_classfr        0.4990752                      0.266
## MFO###myMFO_classfr              0.5000000                      0.473
## Final.All.X##rcv#glmnet                 NA                     18.334
##                            max.Accuracy.fit opt.prob.threshold.fit
## All.X##rcv#glmnet                 0.6055195                   0.50
## Low.cor.X##rcv#glmnet             0.6050093                   0.50
## Max.cor.Y.rcv.1X1###glmnet        0.6162494                   0.55
## Max.cor.Y##rcv#rpart              0.6104535                   0.50
## Random###myrandom_classfr         0.5789474                   0.40
## MFO###myMFO_classfr               0.5789474                   0.40
## Final.All.X##rcv#glmnet           0.6003805                   0.55
##                            opt.prob.threshold.OOB
## All.X##rcv#glmnet                            0.55
## Low.cor.X##rcv#glmnet                        0.45
## Max.cor.Y.rcv.1X1###glmnet                   0.40
## Max.cor.Y##rcv#rpart                         0.40
## Random###myrandom_classfr                    0.40
## MFO###myMFO_classfr                          0.40
## Final.All.X##rcv#glmnet                        NA
## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   D   R
##         D  97 117
##         R  94 194
##     err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy        7.086145        3.238914        10.35792              NA
## PKn       24.399436        6.726635        30.86897              NA
## SKy       28.898436       11.815230        40.37918              NA
## N         54.104346       13.604103        66.70618              NA
## MKn      104.765360       35.796619       139.88924              NA
## SKn      386.381033       95.720498       479.65189              NA
## MKy      308.646219       72.981800       380.01349              NA
##     .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## PKy    0.007153807     0.01195219    0.009646302     14        3        3
## PKn    0.025549310     0.02589641    0.024115756     50        8        7
## SKy    0.031170158     0.04581673    0.043408360     61       10       17
## N      0.059274400     0.05577689    0.053054662    116       21       12
## MKn    0.115482882     0.14940239    0.151125402    226       19       75
## SKn    0.413898825     0.40039841    0.405144695    810      117      135
## MKy    0.347470618     0.31075697    0.313504823    680       31      164
##     .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy      6       13        7      6     14      6     20        0.5398191
## PKn     13       40       23     15     50     15     63        0.5174334
## SKy     23       43       41     27     61     27     84        0.5137057
## N       28       59       85     33    116     33    144        0.4858608
## MKn     75      123      178     94    226     94    301        0.4772882
## SKn    201      454      557    252    810    252   1011        0.4762214
## MKy    156      306      530    195    680    195    836        0.4678321
##     err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy        0.5061532               NA        0.5178958
## PKn        0.4879887               NA        0.4899836
## SKy        0.4737449               NA        0.4807045
## N          0.4664168               NA        0.4632373
## MKn        0.4635635               NA        0.4647483
## SKn        0.4770136               NA        0.4744331
## MKy        0.4538915               NA        0.4545616
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##       914.280975       239.883799      1147.866857               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##         1.000000         1.000000         1.000000      1957.000000 
##         .n.New.D         .n.New.R           .n.OOB         .n.Trn.D 
##       209.000000       413.000000       502.000000      1038.000000 
##         .n.Trn.R           .n.Tst           .n.fit           .n.new 
##      1421.000000       622.000000      1957.000000       622.000000 
##           .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean 
##      2459.000000         3.478161         3.328772               NA 
## err.abs.trn.mean 
##         3.345564
## [1] "Features Importance for selected models:"
##                                All.X..rcv.glmnet.imp
## Hhold.fctrPKn                             100.000000
## Q113181.fctrYes                            64.984274
## Q115611.fctrYes                            52.729697
## Q113181.fctrNo                             51.264404
## Q115611.fctrNo                             44.707621
## Q98197.fctrNo                              40.627886
## Q101163.fctrDad                            27.773431
## Hhold.fctrMKy                              25.424438
## Q116881.fctrRight                          23.579086
## Q106272.fctrYes                            22.437747
## Hhold.fctrPKy                              17.918579
## Q106997.fctrGr                             15.677653
## Q110740.fctrPC                             11.933368
## Q99480.fctrNo                               6.082982
## Income.fctr.Q                               5.630958
## Q98869.fctrNo                               5.552206
## Hhold.fctrPKn:.clusterid.fctr2              0.000000
## Hhold.fctrMKy:.clusterid.fctr4              0.000000
## Q108855.fctrYes!                            0.000000
## Q106272.fctrNo                              0.000000
##                                Final.All.X..rcv.glmnet.imp
## Hhold.fctrPKn                                     81.86284
## Q113181.fctrYes                                   33.28050
## Q115611.fctrYes                                  100.00000
## Q113181.fctrNo                                    61.58911
## Q115611.fctrNo                                    30.25038
## Q98197.fctrNo                                     62.40403
## Q101163.fctrDad                                   44.78129
## Hhold.fctrMKy                                     12.75156
## Q116881.fctrRight                                 60.37796
## Q106272.fctrYes                                    0.00000
## Hhold.fctrPKy                                     21.71103
## Q106997.fctrGr                                    23.73294
## Q110740.fctrPC                                    26.95620
## Q99480.fctrNo                                     28.39881
## Income.fctr.Q                                     16.36844
## Q98869.fctrNo                                     43.16389
## Hhold.fctrPKn:.clusterid.fctr2                    99.86214
## Hhold.fctrMKy:.clusterid.fctr4                    36.43439
## Q108855.fctrYes!                                  15.06953
## Q106272.fctrNo                                    13.95534
## [1] "glbObsNew prediction stats:"
## 
##   D   R 
## 209 413
##                   label step_major step_minor label_minor     bgn     end
## 20     predict.data.new          9          0           0 247.589 259.122
## 21 display.session.info         10          0           0 259.122      NA
##    elapsed
## 20  11.533
## 21      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                        label step_major step_minor label_minor     bgn
## 14                fit.models          7          0           0 113.283
## 11              cluster.data          4          0           0  12.802
## 12   partition.data.training          5          0           0  61.840
## 15                fit.models          7          1           1 171.205
## 18         fit.data.training          8          0           0 213.910
## 20          predict.data.new          9          0           0 247.589
## 16                fit.models          7          2           2 203.254
## 19         fit.data.training          8          1           1 240.509
## 17                fit.models          7          3           3 210.865
## 13           select.features          6          0           0 110.583
## 1                 scrub.data          1          0           0  10.382
## 9       extract.features.end          2          6           6  11.569
## 10       manage.missing.data          3          0           0  12.245
## 8    extract.features.string          2          5           5  11.512
## 2             transform.data          1          1           1  11.299
## 7      extract.features.text          2          4           4  11.467
## 5     extract.features.image          2          2           2  11.399
## 4  extract.features.datetime          2          1           1  11.368
## 6     extract.features.price          2          3           3  11.440
## 3           extract.features          2          0           0  11.352
##        end elapsed duration
## 14 171.204  57.922   57.921
## 11  61.839  49.037   49.037
## 12 110.582  48.743   48.742
## 15 203.253  32.048   32.048
## 18 240.508  26.598   26.598
## 20 259.122  11.533   11.533
## 16 210.864   7.610    7.610
## 19 247.589   7.080    7.080
## 17 213.910   3.045    3.045
## 13 113.283   2.700    2.700
## 1   11.298   0.916    0.916
## 9   12.244   0.675    0.675
## 10  12.802   0.557    0.557
## 8   11.568   0.056    0.056
## 2   11.352   0.053    0.053
## 7   11.511   0.045    0.044
## 5   11.440   0.041    0.041
## 4   11.398   0.030    0.030
## 6   11.466   0.026    0.026
## 3   11.368   0.016    0.016
## [1] "Total Elapsed Time: 259.122 secs"

##                            label step_major step_minor      label_minor
## 5         fit.models_0_Low.cor.X          1          4           glmnet
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 2               fit.models_0_MFO          1          1    myMFO_classfr
## 1               fit.models_0_bgn          1          0            setup
##       bgn     end elapsed duration
## 5 144.381 171.189  26.809   26.808
## 4 128.246 144.380  16.134   16.134
## 3 120.619 128.245   7.627    7.626
## 2 113.845 120.618   6.773    6.773
## 1 113.809 113.845   0.036    0.036
## [1] "Total Elapsed Time: 171.189 secs"